Personalized heart rhythm therapy

ABSTRACT

Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims the benefit of U.S. ProvisionalApplication 63/175,986 filed on Apr. 16, 2021, which is incorporated byreference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to a non-invasive medicaldevice and, more specifically, to a body surface device that may be usedin place or in conjunction to a catheter for treating electrical rhythmdisorders.

BACKGROUND

Conventional devices for treating patients with heart rhythm disordersare invasive and often associated with known risks and drawbacks. Insome cases, patients may be resistant to invasive therapies. For otherpatients, the chance of failure in an invasive surgery is often notinsignificant. To reduce potential injury caused by an invasiveprocedure, the sizes of the invasive surgical devices have becomeincreasingly smaller. However, those conventional devices still facechallenges in effectiveness, navigation inside a body, andidentification of key regions of interest to guide therapy. Cardiacablation for heart rhythm disorders (e.g., arrhythmias) is an invasiveprocedure in which probes are advanced from leg veins percutaneously tothe heart to cauterize or freeze regions of the heart causing thearrhythmia. Ablation performed with a catheter guided is costly andassociated with some risk of complications. Conventional devices areoften guided by mapping sensors that are only able to provide datarelated to the patients when devices are inside the patients' bodies.Those data, despite useful for the physicians, are often insufficientfor the physicians to determine the best course of therapy and sometimeseven the correct region of interest to perform the surgery. The dataalso may not provide a sufficiently comprehensive picture of thepatient's conditions and diseases. For example, invasive devices arenecessarily often small and only provide a limited spatial field of viewof a very localized region of a subject's organ. They can only beinserted for short periods of time, which may miss periods when thepatient actually experiences a problem. Finally, invasive devices arepart of in-hospital diagnostic studies which may not be practical forpatients in remote or rural areas, and are also expensive. Thoseinvasive devices thus limit access to care.

SUMMARY

In accordance with some embodiments, a system for determining apersonalized therapy for heart rhythm disorders for a subject isdescribed. The system may include a non-invasive body surface devicecarrying a plurality of electrodes configured to be in contact with abody surface of the subject. The electrodes may be configured to cover aspatial projection of at least a majority of a heart chamber projectedon the body surface. The electrodes are capable of detecting a pluralityof electrical signals generated by the heart of the subject. The systemmay also include a computing device configured to receive signal datagenerated from the body surface device. The computing device includes aprocessor and memory. The memory, when executed by the processor, causesthe processor to perform operations that include determining locationsof beats that initiate onset of a heart rhythm disorder based on thesignal data and determining locations of sources for the heart rhythmdisorder based on the locations of beats. The non-invasive body surfacedevice may be used in place of or in conjunction with a sensingapparatus inside the heart (such as a catheter) to identify key regionsof interest and guide the physician towards critical regions fortreatment, that is the system provides directionality analysis. The bodysurface device can be worn continuously to monitor the subject longbefore an invasive device is placed in the body, thereby providing amore comprehensive set of data for determining a personalized therapy.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which:

FIG. 1A is a block diagram illustrating a system environment of a heartrhythm monitoring system and a workflow of a fully remote heart rhythmevaluation pathway that is enabled by a non-invasive body surfacedevice, in accordance with one or more embodiments.

FIG. 1B is a conceptual block diagram illustrating the conventionalclinical workflow.

FIGS. 2A and 2B are conceptual diagrams illustrating a non-invasive bodysurface device for detecting a rhythm location (e.g., a heart rhythmlocation) of a subject, in accordance with one or more embodiments.Atrial fibrillation is shown.

FIG. 3A illustrates an example embodiment of a full-torso body surfacedevice, in accordance with one or more embodiments.

FIG. 3B illustrates an example embodiment of a targeted body surfacedevice that is designed for focused regions of the torso, in accordancewith one or more embodiments.

FIG. 4 is a conceptual diagram illustrating a body surface mappingmethod to enable several electrical pathways on the heart to bevisualized on the body surface using signals from a body surface device,in accordance with one or more embodiments.

FIG. 5A is a diagram illustrating an algorithm process to classifylocations of abnormal rhythm to be located inside the heart using thebody surface recording alone and/or intracardiac signals from a device,in accordance with one or more embodiments.

FIG. 5B is a diagram illustrating an algorithm process to extractspecific rhythm signatures in the body surface alone and/or intracardiacsignals using reconstructed signals, and an algorithm able to use thesespecific signatures to refine the rhythm identification, in accordancewith one or more embodiments.

FIG. 5C includes diagrams illustrating the performance of rhythmsignatures identified from the body surface and/or intracardiac signalsto identify the condition of atrial fibrillation, in accordance with oneor more embodiments.

FIG. 6 illustrates a structure of an example neural network isillustrated, in accordance with one or more embodiments.

FIG. 7 is a flowchart depicting an example process that is executable bysoftware algorithms for a computing system (e.g., computing server) toprovide one or more arrhythmia management recommendations based on datacollected by a body surface device, in accordance with one or moreembodiments.

FIG. 8 is a conceptual diagram illustrating personalized guidance ofablation therapy, in accordance with one or more embodiments.

FIG. 9A is a graphical illustration of a flowchart depicting an exampleprocess that is executable by software algorithm for a computing systemto perform a directional guidance for arrhythmias, in accordance withone or more embodiments.

FIG. 9B is a graphical illustration of a flowchart depicting an exampleprocess that is executable by software algorithms for a computing systemto integrate use of body surface or internal catheter systems fordirectional guidance for arrhythmias, in accordance with one or moreembodiments

FIG. 9C is a graphical illustration of a flowchart depicting an exampleprocess that is executable by software algorithm for a computing systemuse guidance from a catheter inside the heart to guide an ablationcatheter inside the heart.

FIGS. 10A, 10B, 10C, 10D, and 10E are various graphical illustrations ofexamples of patients with heart conditions, in accordance with someembodiments.

FIG. 11 is a block diagram of an exemplary embodiment of a generalcomputer system.

In each figure, there can be more or fewer components/steps than shown,or certain components/steps can be replaced with others or can beorganized or ordered in a different manner than is shown.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The figures and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality. The figuresdepict embodiments of the disclosed system (or method) for purposes ofillustration only. One skilled in the art will readily recognize fromthe following description that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles described herein.

Overview

In some embodiments, a device or a method that can identify in advanceif a desired ablation approach will be successful in a given patient isdisclosed. In some embodiments, the device is non-invasive, such as adevice that can be worn or used externally. In some embodiments, thenon-invasive device can indicate if a patient with atrial fibrillation(AF) will respond to pulmonary vein isolation (PVI). In someembodiments, the non-invasive device can indicate if a patient requiresablation in the left side of the heart, which may require more elaborateequipment and more time than ablation on the right side of the heart.The device can identify the appropriate region(s) for ablationpersonalized for an individual, even for complex rhythm disorders. Inthis way, the device can simplify the workflow for managing patientswith heart rhythm disorders.

In some embodiments, a device can identify and locate critical regions(source or dominant regions) for biological rhythm disorders. The devicerecords electrical signals and relates this to known and machine-learnedpatterns of critical regions. For example, in some embodiments, thedevice is a portable or wearable device that can identify regions wherea heart rhythm disorder initiates or is maintained in a specificindividual, distinct from other patients. Having identified theseregions based on detected signals, the device indicates if therapy at adesired location will be effective, allowing the patient to avoid anunnecessary surgical procedure if it will not be effective, whichconventionally the patient would have had to undergo to determine if thetherapy would be effective. This has benefits when deciding when or howto perform an invasive procedure.

Additionally, or alternatively, the device provides navigationalguidance towards these important regions to enable treatment of theseregions. These steps can also be estimated based on knowledge of howpatients with similar data patterns respond to therapy, rather than onactual electrical patterns recorded in that patient. Severalsensor/therapy device designs are specified.

The system and method described herein thus provide a process forpersonalized therapy for heart rhythm disorders, which may also includea combination of lifestyle changes, medications, electrical ormechanical therapy, surgical or minimally invasive ablation, genetic orstem cell therapy.

Some embodiments employ non-invasive or invasive tools to identifypatients in whom ablation therapy for complex rhythm disorders is likelyto succeed. In patients amenable to ablation therapy, some embodimentsinclude a device to map electrical patterns and provide directionalguidance to move a device in three dimensions towards optimal locationsfor therapy. Some embodiments may provide the ability to deliver therapydirectly to tissue at this location.

In some embodiments, the process has the ability to deliver personalizedtherapy using data from the current individual but also to estimatetherapy using machine learning of data from other individuals withsimilar profiles based on a digital classification that can be updatedusing strategies such as crowd-sourcing.

The process may apply to disorders of heart rhythm, mechanicalcontraction, or heart failure. Other exemplary applications includeseizure disorders of the brain, diseases of gastro-intestinal rhythmsuch as irritable bowel syndrome, and bladder disease including detrusorinstability. The process may apply to chaotic disorders in these organs,such as atrial fibrillation in the heart or generalized seizures in thebrain, as well as simple rhythm disorders. These examples are in no waydesigned to limit the scope of the disclosure for other conditions. Thepersonalization aspect is suited for disorders that are heterogeneoussyndromes rather than a single disease entity.

The process may identify patients in whom critical regions for a heartrhythm disorder arise near standard therapy targets or not. An exampleof this embodiment is to identify patients with AF who are likely tobenefit from PVI. In patients who are unlikely to benefit from PVI, thedevice identifies those with localized sources in other regions of theheart that may be amenable to ablation. In those in whom such localizedsources are not identified, the device identifies patients in whomdefined therapy lesion sets corresponding to Maze surgery may work. Thedevice can identify patients with other heart rhythm disorders such asventricular tachycardia or with atypical atrial flutter in whom ablationwill or will not be successful.

The process and the system that includes a body surface device maydetermine one or more locations of the heart that are associated with aheart rhythm disorder. The locations may include sites of origin andsource regions of interests.

Sites of origin of a heart rhythm disorder may include the sites wherethe first beat or beats (within the first 30 seconds, typically thefirst 5-10 beats) which initiate the heart rhythm disorder in question,distinct from normal sinus rhythm. Site of origin may also be referredto as locations of beats that initiate onset of a heart rhythm disorder.For instance, AF often initiates from normal rhythm by a few (betweenone and about a dozen) premature atrial beats, which often occur at oneof the pulmonary vein regions of the heart. The device is capable ofidentifying these originating or triggering beats. If these beats arisefrom the pulmonary veins, ablation to isolate the pulmonary veins andeliminate these triggers may be effective. In another patient in whommany or most trigger beats do not arise from the pulmonary veins, PVImay not be effective.

Source regions of interest are different from sites of origin. Thesource regions of interest, or referred to as locations of sources,indicate which regions of the heart may drive the heart rhythm disorder.Source regions can be identified during a heart rhythm disorder aspatches of organized activity (a) within chaotic disorders such asatrial fibrillation in the heart, or (b) from which activation emanatesto drive organized rhythms such as atrial tachycardia or ventriculartachycardia, from focal activity or small recirculating circuits knownas reentrant circuits. In some embodiments, the process uses analyticaltools including signal processing, artificial intelligence and machinelearning to detect organized patches.

Organized patches may represent rotational activity, focal activity,repetitive activity of neither pattern, or other forms of organization.In most rhythms, a source would be a focal or reentrant (rotational)site. For atrial fibrillation (AF), sources may be any of thesepatterns. Sources that arise near regions that would be targeted bystandard therapy, such as pulmonary veins in AF, a scar isthmus for VTor a focal brain lesion for seizure disorders and may not requireadditional therapy. Sources that arise from sites outside of thesestandard targets are often difficult to find, yet may be identified bythis invention so that they may be targeted for additional therapy. Thisinformation is conveyed to the operator.

In some embodiments, the process may identify a hierarchy of heartrhythm sources, pointing out the most important for therapy. For atrialfibrillation, this differs from the prior art that often recommendstreating all detected sources. The conventional prior art processrequires mapping, detection and therapy of less-critical regions, whichmay be time consuming, adds difficulty to the procedure, and may haveadverse effects. Less-critical regions identified by the prior art maybe false-positives that do not require therapy.

In some embodiments, a device can identify the most important sourceregions for the heart rhythm disorder by quantifying their size or areawithin the heart chamber, or using another feature. This can be appliedto organized drivers for a heart rhythm disorder such as atrialfibrillation or ventricular fibrillation. This also applies to thesource driving tonic/clonic seizures in the brain. This also applies toa focus that drives irritable bowel syndrome. This hierarchy of sources,from most to least dominant, is conveyed to the operator and can be usedfor treatment planning.

In some embodiments, the process may map critical regions for biologicalrhythm disorders within the entire heart without the need for wide-areacatheters such as a basket, which are cumbersome, may not cover theentire organ, and typically cannot deliver therapy. In some embodiments,the process uses non-invasive body surface potential mapping as acomplement to or even a replacement of mapping from a smaller catheterinside the heart. The body surface map provides a global view of theheart rhythm disorder, which complements an intracardiac catheter. Therelative sizes of these fields of view can be complementary, such as aglobal map from the body surface, and a catheter inside the heart whichcan provide a limited spatial field of view at high resolution.

A catheter may use a mapping spade placed within the heart that isphysically large enough to cover the source region of simple or complexrhythm disorders, yet small enough for high-density recordings from aplurality of electrodes. The size of this intracardiac system can bepersonalized to the type of rhythm. The range of electrodes for thisintracardiac system is from 4 to 128. An exemplary dimensional range forthe mapping spade for heart arrhythmia applications is on the order of 1cm×1 cm to 3 cm×3 cm (W×L). A typical arrangement for mapping AF sourceswould be 16-64 electrodes in an area of 4 cm² to 9 cm². A typicalarrangement for mapping gaps in a pulmonary vein encircling line wouldbe 4-16 electrodes in an area of 1-2 cm². A typical arrangement formapping critical regions for ventricular tachycardia would be 9-25electrodes in an area of 2-4 cm². The size of this spade can also bepersonalized to the profile of the patient, using tools such as machinelearning calibrated to patients of similar clinical type and data. Thesize of the spade will vary with the organ being treated. The size maybe smaller for a device in the brain, where small size is at a premiumto avoid destruction of tissue, than for a device in the heart, wherelarger mapping and ablation areas are sometimes needed. The therapy toolcontacts the organ by conforming to its surface at a plurality oflocations.

In some embodiments, a non-invasive body surface mapping device uses aplurality of carefully placed electrodes on the body surface to map theheart rhythm disorder. In the prior art this typically needs anatomicalinformation of the patient from detailed computed tomography (CT) ormagnetic resonance imaging (MRI) data.

Conversely, in this device the resolution needed to identify importantpatient groups or rhythm types is fulfilled without the need forcomputed tomography (CT) scan or magnetic resonance imaging (MRI) data.This increases the usability of the approach over existing methods basedon medical image analysis (CT or MRI scans), since the body surfacedevice is now fully wearable and suitable for fully outpatient usewithout hospital visits for imaging. This is an advance over prior artmethods such as Electrocardiographic Imaging (ECGI).

In some embodiments, separating rhythms arising from the left side ofthe heart versus the right side of the heart can be achieved without CTor MRI data. Similarly, separating originating beats from pulmonary veinregions of the left atrium (that project to the back of the chest) fromother regions of the heart, can be achieved by body potential surfacemaps without CT or MRI data.

Heart and torso anatomy for the implementation without CT or MRI datacan be obtained from multiple sources. An embodiment may use anatomyfrom stored databases representing standardized human anatomies andtherefore not extracted from the specific patient anatomy. The storeddatabases may represent relationships between the heart, surroundingtissue and body surface accurately enough to be used for many purposes.As a next step, the data can be matched to a patient under considerationbased on gender, chest diameter, height and weight. In some embodiments,this is sufficient to identify if a heart rhythm disorder originates inthe left atrium, right atrium, left ventricle or right ventricle. Inanother embodiment, this generalized anatomical data is sufficient toidentify if originating beats of atrial fibrillation arise from thepulmonary vein regions, that nearly always enter into the back of theleft atrium, from other sites in the left or right atria. In anotherembodiment, this generalized anatomical data is sufficient to identifyif beats of ventricular tachycardia arise from the right or leftventricle of the heart. Other applications of the generalized anatomicaldata integration will be apparent to one skilled in the art.

Various embodiments may use three different approaches to providenavigational guidance for a sensor or ablation probe without firstcollecting data using cumbersome global catheters inside the heart. Oneapproach uses data from the body surface device. Another usessophisticated directionality analysis from the electrode device insidethe heart. A third combines data from both the body surface andelectrode device within the heart.

In some embodiments, the devices may perform directionality analysisfrom the body surface. For example, a body surface device may identifythe location of critical regions for the heart rhythm disorder. Thedevice then calculates the direction or vector of each critical regionfrom or to using a probe such as external ablation sources forradiofrequency ablation or intracardiac ablation catheters. This is usedto provide directionality guidance for the operator to move said probetowards said critical region. Directionality greatly advances theembodiments over the prior art where the entire organ had to be mappedto identify a location of interest. One analogy is a satellitenavigational system which computes directional guidance to enable a userto get from position A to B. The prior art required A and B to beidentified from a map followed by interpretation by the user to whowould have to infer directionality information themselves.

The directional guidance is enabled by a knowledge of what sourcesignals should be like when actually at the source, and when at adistance. This knowledge enables the system to indicate when therecording system is directly over the source. If the recording system isat a distance, then the recording system indicates directionalitytowards the source.

In some embodiments, the directional guidance is tailored by additionaldata beyond recorded signals. Such data are created as personal digitalrecords for an individual. The personal digital records may captureclinical, pathophysiological, laboratory, genetic or cellular datarelevant to the disease being treated. This is pertinent to diseaseswith considerable variability in treatment outcome, such as heart rhythmdisorders, that reflect varying patient profiles. For instance, a sourcemay be near the pulmonary veins in patients with a certain profile, yetaway from the pulmonary veins in patients with different profiles.Similarly, a source for ventricular tachycardia may be in the leftventricle in patients with certain profiles and right ventricle inpatients with different profiles. Personal digital records may be usedfor data in precision medicine. This may take the form of a digitalportrait of an individual by capturing data from real-time sensorstreams, clinical profiles, demographics, data in electronic healthrecords, complex data from imaging or genomic analysis. In general,clinical or laboratory data will be available most often, while genomicdata may be unavailable for many patients.

Personal digital records can be used to decipher patterns of heartrhythm disorders difficult to understand by experts. Examples includeidentifying AF patients who will respond to PVI ablation, or VT patientswho will respond to ablation. Another example is whether a patient withAF and particular signals within the heart, and a specific profile ofage, gender and other diseases, is likely to respond to PVI therapy. Yetanother example is whether AF in a patient with AF may be caused byrotational circuits, focal circuits, repetitive patterns, partialrotational or focal circuits, “random” activity, electrical propagationaround areas of scar, or specific anatomical sites.

In some embodiments, techniques such as machine learning are used toclassify an individual's personal digital records using a database ofprofiles associated with response or no response. Machine learning maybe trained by objective and clinically relevant labels such assuccessful response to therapy (e.g., elimination of AF by PVI ablation,elimination of VT by ablation, improvement in left ventricular ejectionfraction by ablation of heart rhythm disorder), or adverse response totherapy (e.g., prolongation of the QT interval by pharmacologicalagents, failure from to ablation). The machine learning model can nowmake a prediction for an individual, essentially finding their closestmatch. This trained machine learning model structures the database intoa digital classification for that disease stratified by an outcome suchas success or failure from therapy. Personal digital records then encodedata relevant to therapy of that disease, which can be numericallymatched to personal digital records of a large population to predictpatient outcome.

Personal digital record analysis may be used to improve navigationwithin the heart to regions of interest, to identify sources, predictingthe type and size of sources, and predicting the response of sources totherapy. The classification matches specific patterns of electricalsignals and clinical profiles to success or failure of drugs, ablation,maze surgery or other therapy. This personalization of therapy is basedon integrating data across several biological scales.

In this way, the device does not focus only on signals at the device,but takes into account modifying factors from the patient's profile.This profile is a novel combination of patient-related data at theclinical level, at the tissue level (e.g. signals, imaging data of theheart) and at the cellular level (e.g. biomarkers in the blood, unusualsignals such as monophasic action potentials). By using machinelearning, the device individualizes treatment and does not cater just tothe statistical majority of individuals who respond to a therapy. Thisis another form of using FAIR software methods (Findable, Accessible,Interoperable, and Reusable) to reduce bias—for instance, to catertherapy to an individual even if they differ demographically orphysiologically from the ‘average’ (majority) of patients in apopulation. Machine learning provides one approach to achieve the goal.

Personalization can be encoded by computer and analytical methods basedon associative algorithms, data clusters including unsupervised machinelearning, semi-supervised machine learning, and supervised machinelearning and networks trained by labeled events in similar anddissimilar individuals. The tailoring of personal digital records totherapy is enabled by partitioning data with labels of ‘healthful vsdisease’, ‘responsive to therapy vs non-responsive’, or multiclassresponse to therapies labeled such as ‘therapy 1’, ‘therapy 2’, . . . ,‘therapy n’. Analysis can be one or more of supervised machine learning,neural networks, unsupervised machine learning, cluster analysis,correlation analyses, logistic regression analyses, decision trees, timedomain analyses, frequency domain analyses, trigonometrictransformations, and logarithmic transformations.

Personalization for heart rhythm may use signals that capture therhythm. This may include electrical potentials (electrograms) from anon-invasive device or invasive device within or adjacent to the heart.Other signals that can be analyzed include heat (infrared), mechanicalmotion (piezoelectric or other sensors), chemical composition, bloodflow and pressure (hemodynamics), wall tension (cardiac contractilityand relaxation), Cardiac Images (magnetic resonance imaging, computedtomography), or other indices that may have diagnostic value. Moredetailed data includes three-dimensional anatomical and structuralabnormalities. Clinical data can be extracted from history and physicalexamination, indices of pathophysiological comorbidities, blood andtissue biomarkers, and genetic and cellular makeup of an individual.Non-invasively, sensors may record the standard electrocardiogram,surface recordings from higher resolution body surface potential mapping(e.g., multiple ECG electrodes) or ECG imaging, cutaneous measures ofnerve activity. Reflectance on the skin to visible light or otherelectromagnetic waveforms can be used to measure signals that indicateheart beats, either regular or irregular. This can be detected usingphotoplethysmography (PPG) or other forms of detecting reflectance.Visible light in the near-infrared portion of the spectrum may be usefulfor this. Other types of sensed signals that may be used will beapparent to one of skill in the art.

In some embodiments, a system may include a processor and a memorystoring instructions that, when executed by the processor, performoperations including detecting bodily signals associated with one ormore bodily functions at one or more sensors associated with the humanbody, processing the bodily signals to create one or more sensedsignatures, processing the signatures using the digital object todetermine an effector response, delivering one or more effectorresponses to control a bodily task and monitoring said response.

In some embodiments, a process can identify individuals amenable totherapy for treating complex rhythm disorders, provides directionalguidance in 3 dimensions to move a sensor device towards optimallocations for therapy, and enable therapy to tissue at this location. Insome embodiments, a non-invasive wearable device may be used by thepatient at home, without hospital visits, to determine if ablation islikely to be successful or if drug therapy should be continued. Thisgreatly improves outpatient workflow, and reduces unsuccessfulprocedures by better patient selection. Another embodiment is a systemproviding a personalized diagnosis of rhythm disorders and a ‘singleshot’ sensor/therapy tool. Some embodiments, which are not intended tobe limiting, include cardiac applications in heart rhythm disorders,coronary artery disease and in heart failure.

In some embodiments, the device is artificial intelligence (AI) enablednon-invasive ECG device, simple enough to be applied to the chest orback by the patient at home. The single-use device will be worn for upto several days, will automatically detect the onset and then ongoingepisodes of the heart rhythm disorder, and alert the user whensufficient data is recorded. Data is transmitted to the cloud foranalysis, from which results will be available via electronic healthrecords for review. Analysis can indicate if that patient will respondto ablation, if ablation is needed on the left or right side of theheart, and if they may respond to medications. The physician can thenmake a fully remote care plan, without the need for in-hospitalevaluation or invasive testing. This is useful to streamline costs,provide access to patients in rural areas, or who may not have resourcesto take time off to visit the hospital, and to minimize hospital contactduring public health emergencies such as the COVID pandemic. One targetindication is whether to refer an AF patient directly to pulmonary veinisolation (PVI), advanced ablation, or drug therapy choice. Anothertarget indication is whether to refer a patient with supraventriculartachycardias directly to ablation, which has very high success and iscurative for the rhythm of typical atrial flutter, or to identify thatablation may be more complex and should be tried only if medications donot first work.

In one or more embodiments, the device is a non-invasive electrodeconfiguration worn on the chest, back or other parts of the bodysurface. It may take the form of a patch, or it may be embedded inclothing. The electrode configuration is designed to measure electricalactivity and classify types of specific heart rhythm disorders. Thelocation and configuration are separate for men and women, to optimizerecordings given differences such as breast tissue. A patch hassufficient adhesive to be worn comfortably for several days. In someembodiments, the patch uses straps, such as on the wrist, ankle, chestor other body part without adhesive. Signals are transmitted by physicalwire or wirelessly for analysis. Analysis may include identification ofthe location of beats that initiate the heart rhythm disorder, orregions that sustain heart rhythm disorders, using directional rules andusing machine learning from previously-stored classification of theresponse of patients to various forms of therapy. If the patch is wornduring invasive electrophysiological study, it can provide globalguidance to allow a separate probe or ablation tool to be directedtowards the region of interest to deliver therapy for the rhythmdisorder.

An application in an electronic device such as a smartphone, smarttablet, or smart device can help guide the user and record the necessarypositions of the patches using its optical camera, Lidar sensor(infrared, ultraviolet, or other), or both (only location of electrodeswill be recorded relative to anatomy, photos will not be saved ortransmitted to the Cloud). Appropriate attached and location recordingwill ensure proper processing of data. Alternatively, the device mighthave a built-in indicator to ensure proper positioning and attachment ofthe device.

In some embodiments, “associative learning” may refer to a process oflinking input data with measurable physiology or clinical outcome.Associative learning may be iterative, enabling associations to bemodified (“learned”) based upon patterns of change between input andmeasured output (physiological or clinical endpoints).

In some embodiments, “biological signal” may refer to a signal producedby the body of a subject, and may reflect the state of one or morebodily systems. For instance, the heart rate reflects cardiac function,autonomic tone and other factors.

In some embodiments, “biometric signals” may refer to signals thatprovide metrics of human characteristics. Biometric identifiers can bephysiological or behavioral. Physiological biometrics include, but arenot limited to, DNA, fingerprints or palm prints, mouth swabs, tissue orurine samples, retinal images, facial recognition, the geometry of handsor feet, recognition of the iris or odor/scent of an individual.Physiological biometrics may also include signals such as vital signs,the ECG, the EEG, EMG, and so on. Behavioral biometrics include patternssuch as gait during walking or typing rhythm. Embodiments described inthis disclosure may use dynamic patterns of combined physiological andbehavioral biometrics over time, which adapt to changes in theindividual and are thus robust to forgery from prior “versions” of aperson's signature.

In some embodiments, “body” may refer to the physical structure of ahuman or an animal for veterinary work.

In some embodiments, “Body Surface Potential Map” (BSPM) or “Bodysurface map” may be generated by using multiple electrodes on a bodysurface to provide a high-resolution picture of heart rhythms thanavailable from the standard ECG. The range of leads needed for BSPMranges from 8 to >250. In some embodiments the number of leads is —50,often <16. Leads are typically placed on the chest, back, sides of thetorso and shoulders. In some embodiments, a smaller electrodedistribution that covers the projection on the body surface of themajority of at least one heart chamber is used. Some technologiesrequire computed tomography (CT) or magnetic resonance imaging (MRI) ofthe heart to map heart rhythms, like electrocardiographic imaging(ECGI). In some embodiments of the current invention, CT or MRI are notneeded to map heart rhythms.

In some embodiments, a “consumer device” may refer to a device that isavailable directly to a consumer without a medical prescription.Historically, such devices typically were not regulated by a medicalregulatory agency or body, such as the U.S. Food and Drug Administration(FDA) or similar regulatory bodies in other countries. However, somedevices are FDA cleared. A Consumer device may include hardware,software, or a combination thereof. It is typically not a medicaldevice, the latter being defined as an instrument, apparatus, implement,machine, contrivance, implant, in vitro reagent, or another similar orrelated article, including a component part, or accessory, which isintended for use in the diagnosis of diseases or other conditions, or inthe cure, mitigation, treatment, or prevention of disease, in man orother animals.

In some embodiments, “data streams” or “stream(s) of data” or “data” mayrefer to biological data sensed by one or more sensors that can providereal-time or near-real-time information on the biological process beingsensed. Sensors in the heart may provide data comprising theelectrocardiogram (ECG), Electrogram (EGM), pulse rate, pulse waveformand cardiac hemodynamics Other data may include cardiac acoustics,including analysis of heart sounds, murmurs and sophisticated analysesof hemodynamics related to the heart. Lung function may be sensed aschest movement, auscultatory sounds and nerve firing associated withbreathing. Gastrointestinal disease may be sensed as sounds(borborygmi), movement on the abdominal wall, and electrical signalsrelated to smooth muscle activity of the gut. Central and peripheralnervous system activity may be sensed as nerve activity on the scalp(electroencephalogram, EEG), remote from the scalp but still reflectingthe EEG, and from peripheral nerve firing.

In some embodiments, “demographics” may refer to personal informationwhich may include, but is not limited to, age, gender, family history ofdisease, ethnicity, and presence of comorbidities and which may beclinically relevant.

In some embodiments, “digital classification” may refer to a partitionof different states of disease or health based on mathematical indexes.Traditional disease classifications are qualitative, such as “atrialfibrillation is more common in the older individuals, those with heartcomorbidities such as valvular lesions or heart failure, those withmetabolic syndrome”. A digital classification translates this broaddataset into quantifiable primary and secondary data elements (datavectors). The likelihood that a disease entity D_(n) is present in aspecific individual is approximated by the probability P(D_(n)):

${p\left( D_{n} \right)} = {\sum\limits_{i = 1}^{m}\frac{\left( {k_{n}{p\left( V_{n,i} \right)}} \right)}{k_{n}}}$

Where m is the number of available data input types, n is the diseasebeing considered, and p(V_(n,i)) is the probability that data vectorV_(n,i) contributes to disease n for input i, and k_(n) is a weightingconstant for disease n. These elements are integrated into theclassification, which computes probabilities that a specific data inputcontributes to disease. Probabilities can be obtained from populationdata, in which the profile of a specific person is matched to themost-similar individuals or profiles in that population. The probabilitycan also be obtained from data in this individual alone, compared totimes of health (self-reported or adjudicated) and times of disease(self-reported or adjudicated). These calculations can be performed bytraditional estimating equations but may also by statistical techniquesand machine learning. A digital classification (i.e. a classification)represents a disease entity stochastically by the aggregate ofabnormalities in multiple related data inputs. This process is dynamicsince the equation reflecting disease will change when data is added,when data changes, and when the state of health or disease is updated.This is an approach to integrate massive amounts of data fromtraditional data sources as well as wearable devices in an individual,or massive amounts of data from several individuals as a crowd-sourcedparadigm.

In some embodiments, “electrocardiographic imaging (ECGI)” may refer toa data source that refers to a process that records body surfacepotentials on the chest then uses mathematics to calculate electricalactivity at precise regions of the heart. The inverse solution developsmathematical transforms that may need detailed knowledge of anatomyinside the chest, typically provided by computed tomography (CT) ormagnetic resonance imaging (MRI), or from standardized anatomicaldatabases, and make assumptions about their conductivity, resistance andother electrical properties. In this way, body surface potentials can bemapped to the heart.

In some embodiments, an “electrocardiographic (ECG) patch” may refer toa device that includes electrodes to sense cardiac rhythm. The ECG patchmay be a data source. The ECG patch may be placed in regions of thebody, such as on the back. Depending on the body placement andapproaches used to analyze data generated by the ECG patch, the ECGpatch can discriminate heart rhythm activation patterns of interest. Insome embodiments, an ECG patch on the back can record atrial activationto guide AF therapy, which can be tailored to best record activity inwomen versus men, and for different rhythm applications. The ECG patchdoes not necessarily require CT or MRI imaging for analysis, and is aform of body surface potential mapping without mapping the entire bodytorso.

In some embodiments, “historical data” may refer to stored data, whichmay include reports from medical imaging, e.g., magnetic resonanceimaging (MRI), computed tomography (CT), radiological, or other scans ofan organ, data from genetic testing analyses (e.g., presence of one ormore genomic variants), previously-obtained ECG reports, pathology,cytology, information on genomic variants (genetic abnormalities andnon-disease causing variations), and other laboratory reports. This alsoincludes clinical demographics such as age, gender, other conditionspresent in the individual, and a family history of diseases. Historicaldata may further include additional personal historical details thatcould be relevant to generating the personal digital record, forexample, socioeconomic status including income strata, mental illness,employment in a high-stress profession, number of pregnancies (inwomen), engaging in high-risk behaviors such as smoking, drug or alcoholabuse, etc.

In some embodiments, “machine learning” may refer to a series ofanalytic methods and algorithms that can learn from and make predictionson data by building a model. Machine learning is classified as a branchof artificial intelligence that focuses on the development of computerprograms that can automatically learn to produce predictions whenexposed to data. In some embodiments, machine learning is one tool usedto create the digital network and personal digital records linkingsensed or recorded data with a specific output such as response totherapy, or ability to maintain normal rhythm. For applications in thebrain, outputs could include absence of seizure activity. Machinelearning techniques include supervised learning, transfer learning,semi-supervised learning, unsupervised learning, or reinforcementlearning. Several other classifications may exist.

In some embodiments, “unsupervised machine learning” may include methodsof training of models with training data without the need for traininglabels. Techniques in unsupervised machine learning may include clusteranalysis that may be used to identify internal links between data(regardless of whether data is labeled or unlabeled). In someembodiments, patterns (clusters) could be identified between clinicaldata (such as diagnosis of atrial fibrillation, or presence of heartfailure, or other disease), family history, data from physicalexaminations (such as regularity of the pulse, low blood pressure), datafrom sensors (such as altered temperature, altered skin impedance),electrical data (atrial waveforms on the ECG), imaging data (enlargedleft atrium or reduced), biomarkers, genetic and tissue data asavailable. Another technique is to use autoencoders, to featurize andcompress input data. Autoencoders are sometimes described as‘self-supervised’ since the model input and output are the same.

In some embodiments, “supervised machine learning” may include methodsof training of models with training data that are associated withlabels. Techniques in supervised machine learning may include methodsthat can classify a series of related or seemingly unrelated inputs intoone or more output classes. Output labels are typically used to trainthe learning models to the desired output, such as favorable patientoutcomes, accurate therapy delivery sites and so on. Supervised learningmay also include a technique known as ‘transfer learning’, where apretrained machine learned model trained on one set of input or task, isretrained or fine-tuned to predict outcomes on another input or task.

In some embodiments, “semi-supervised machine learning” may refer to aprocess that combines techniques from supervised and unsupervisedmachine learning to address cases where a large amount of data isavailable but only a portion of the data is labeled. One approach is toimpute or infer labels from similar data, based on a comparison of thedata under consideration to other data within the database. Anotherapproach is to generate labels for an unlabeled dataset based on theportion of data that is labeled. Yet another approach is to use trainingfrom a different problem or a different dataset to generate labels forthese data. Such techniques are used to improve the learning accuracy ofmodels by creating “pseudo labels” for the unknown labels (an approachknown as transductive learning) and to improve model learning by addingin more input to output examples (inductive learning).

In some embodiments, “reinforcement learning” may refer to a form ofmachine learning which focuses on how software agents take actions in aspecific environment to maximize cumulative reward. Reinforcementlearning is often used in game theory, operations research, swarmintelligence and genetic algorithms and has other names such asapproximate dynamic programming One implementation in machine learningis via formulation as a Markov Decision Process (MDP). Reinforcementlearning may differ from supervised machine learning in that it may notuse matched inputs and labeled outputs, and actions that result insub-optimal rewards are not explicitly corrected (unlike supervisedlearning which may correct suboptimal rewards via e.g., back propagationalgorithms in a perceptron).

In some embodiments, a “medical device” may refer to an instrument,apparatus, implement, machine, contrivance, implant, in vitro reagent,or another similar or related article, including a component part, oraccessory, which is intended for use in the diagnosis of disease orother conditions, or in the cure, mitigation, treatment, or preventionof disease, in man or other animals.

In some embodiments, “neural networks” may refer to a class of machinelearning models that include interconnected nodes that can be used torecognize patterns. Neural networks can be deep or shallow neuralnetworks, convolutional neural networks, recurrent neural networks(gated recurrent units, GRUs, or long short term memory, LSTM,networks), generative adversarial networks, and auto-encoders neuralnetworks. Artificial neural networks can be combined with heuristics,deterministic rules and detailed databases.

In some embodiments, personal digital records may include data relatedto health or disease of an individual. The personal digital records mayintegrate several clinical data streams which may or may not includecellular, genomic, proteomic, metabolomic or other data. The personaldigital record may be stratified, partitioned or separated by desiredgroups, such as response to specific therapy, presence of a heart rhythmdisorder, presence or seizure activity of the brain, good health orother attribute in that person. The personal digital record for anindividual can be compared to a digital classification of data from alarge group to identify individuals with ‘similar’ profiles. Thiscomparison to similar profiles may be done mathematically and, oncedone, may enable predictions or selection of optimal therapy based onthe successful response of those similar individuals. In someembodiments, the comparison may take the form of a mathematical ‘bestestimation’ since all required data may not be available in the personaldigital record of a given patient or in the digital classification.

Personal digital records enable personalized medicine in an individual.This is an alternative to the ‘one size fits all’ approach that commonlyapplies one therapy or approach to all patients of a subjective ‘type’.Data elements used to create the personal digital record may representthe individual's health state, weighted by their likely contribution tothe specific disease or index of health being considered. Personaldigital records may be matched to a digital classification by algorithmsthat take into account the calculated or documented probability of theimpact of each data type on health or disease. This may usedeterministic algorithms or iterative processes including machinelearning. For example, a personal digital record for heart rhythm mayprimarily consider heart rate and electrographic signals (surface ECGand intracardiac), and then consider heart function, prior history ofheart rhythm issues, prior therapies, and so on. Greater mathematicalweighting may be given to these data elements. Data from other organsystems can also then be included, and can enable a more comprehensiveassessment and a closer match to other individuals in a digitalclassification. Such other data streams may include changes in breathingrate (e.g., lung sensors), changes in nerve firing rate (e.g., nervefunction). Other data elements may include abnormal cardiac ejectionfraction, location and presence of structural abnormalities of theheart. Historical data including age, gender, medication use, familyhistory, laboratory values and genetic data can also be included in thepersonal digital record.

In some embodiments, “population data” may refer to a determinant of theaccuracy of a process. This is to create a digital classification ofpatients in the population. The classification may include some or alldata elements in the personal digital record of the individual underconsideration. Mathematical analyses are used to compare the personaldigital record of the individual to the digital classification andcalculate the best match. If the index individual is very different fromthe reference population then the digital classification may notadequately represent this individual. In this case, data may be derivedprimarily from that individual, using prior data at times of adjudicatedhealth or adjudicated illness. If the reference population is broad buthas other limitations, such as not having sufficient data points for anaccurate digital classification, or not having well-labeled data, theclassification may be less useful. In some embodiments, the ideal dataset may include data that are well labeled and from a large number ofindividuals that represent the entire population, which can be groupedby desired outcome to create a digital classification.

In some embodiments, “sensors” may include devices that can detectbiological signals from the body of an individual. A sensor may be indirect contact with the body or may be remote. When applied to a groupof individuals, sensors may represent all or part of a definedpopulation. Electromagnetic sensors can sense electromagnetic signalsrelating to the electromyogram (EMG), electroencephalogram (EEG),electrocardiogram (ECG), nerve firing, electromagnetic light (visible orinvisible such as near infrared or infrared) or other emitters. In somecases, the term “sensor”, especially when describing certain cardiacapplications in which electrical information is detected, may be usedinterchangeably with “electrode”, “electrode catheter”, “probe” or“catheter.” Electrical sensors can also detect bioimpedance, such asconductance across the skin that decreases in the presence ofelectrolyte solutions such as sweat when a person perspires, and thatmay occur during times of sympathetic nervous system predominanceSensors can also detect other chemical changes via current flows.Sensors also include devices that detect temperatures, such as athermistor or other thermal detector. Sensors can detect light such aschanges in the color of reflected or emitted light from heart activity(photoplethysmography), changes in peripheral oxygenation (e.g.,cyanosis, anemia, vasodilation on the skin). Sensors can detect soundvia a microphone. This can be used to sense sounds from the heart, lungsor other organs. Sensors can detect contact force, pressure, or othervibrations or movement via piezoelectric elements. Sensors can detectchemicals directly, using specialized sensors for hormones, drugs,bacteria and other elements that are typically transduced on the deviceto an electrical signal. Examples include motion sensing of chest wallmovement from a breath or heartbeat, chest wall vibrations from certaintypes of breath (e.g., a loud obstructive breathing sound) or heartsound (e.g., a so-called “thrill” in the medical literature). Breathsensors can detect movement of the chest wall, abdomen or other bodyparts associated with ventilation, or acoustic data (sound) associatedwith breaths, or oxygenation associated with breathing. Chemical sensorscan detect chemical signals on the skin or other membranes that reflectbody chemistry such as oxygenation and deoxygenation, acidosis (pH),stress (catecholamines), glucose levels, certain drugs or other statesthat will be familiar to those skilled in the biochemistry arts. Sensorscan also detect images using a camera or lens requiring contact from thefingerprint or other body part, or sense movement from specific muscles,or sense iris dilation or oscillations from photosensors in a contactlens. Positional sensors can identify positions of body parts andchanges over time (including gait) or contact sensing of the position ofcertain body parts at one point in time or over time (e.g., a facialdroop, a facial tick or another idiosyncratic movement). In exemplaryembodiments of the inventive system, multiple sensors may be used incommunication with a central computing device or which may form anetwork linked via BLUETOOTH, WI-FI, or other protocol to form anintranet or internet of things (IoT) of biological sensors.

In some embodiments, “Signal” may include electronic, electromagnetic,digital or other information that can be sensed or acquired. Sensedsignals are detected unaltered from their natural form (e.g., recorded)with no transformation. Sensed signals are typically biological signals.Sensed signals can be detected by humans (e.g., sound, visual,temperature) but also machines such as microphones, auditory recorders,cameras, thermometers. Acquired signals are detected in a transformedstate, such as an ECG recording. Such signals may be biological, sincecardiac bioelectricity generates the ECG, or non-biological signals,e.g., vibration sensed after application of sonic or ultrasonic energy,or a haptic signal transduced from a sensed electrical, sonic or anothersignal. Signals may be sensed via physical contact with a sensor.

In some embodiments, “smart data” may refer to application-specificinformation acquired from information sources that can be used toidentify and/or act upon normal or abnormal function in an application.Smart data is thus different from the term “big data”. “Smart data” istailored to the individual, and tailored to address the specific task orapplication—such as to maintain health and alertness or detect and treatdisease such as sleep-disordered breathing, using appropriately tailoredknowledge. Such knowledge may be based on physiology, engineering, orother principles. Conversely, “big data” is often focused on extremelylarge datasets for the goal of identifying statistical patterns ortrends without an individually tailored link. In machine learningparlance, smart data may result from supervised learning of datasets toa known output, while big data simply speaks to the volume of datawithout necessarily implying any knowledge of the significance ofspecific datasets.

In some embodiments, a “subject” may refer to a human or an animal forveterinary work.

Other biological terms take their standard definitions, such as heartfailure, tidal volume, sleep apnea, obesity and so on.

The following description and accompanying figures provide examples ofapplications of the inventive system and method for personalizingtreatment by analyzing personal digital records of health and disease,to detect regions of interest for biological rhythm disorders and treatsuch regions of interest. The examples described herein are intended tobe illustrative only. As will be evident to those of skill in the art,additional variations and combinations may be formed employing theinventive principles disclosed herein.

Example System Environment

FIG. 1A is a block diagram illustrating a system environment 100 of aheart rhythm monitoring system and a workflow of a fully remote heartrhythm evaluation pathway that is enabled by a non-invasive body surfacedevice 110, in accordance with one or more embodiments. In someembodiments, the non-invasive procedure may be replaced or supplementedby an invasive procedure such as a surgery or putting a catheter 115inside the body of the subject 105. In some embodiments, the catheter115 is not needed. The system environment 100 shown in FIG. 1A includesa subject 105, a body surface device 110 attached to the subject's body,a user device 120, a physician 130, a physician device 132, a computingserver 140, a data store 150, and a network 160. In various embodiments,the system environment 100 may include fewer or additional components.The system environment 100 may also include different components.

The subject 105 may be someone who is diagnosed with a health conditionsuch as a heart rhythm disorder or another type of health condition suchas seizure disorders of the brain, diseases of gastro-intestinal rhythmsuch as irritable bowel syndrome, and bladder disease including detrusorinstability. A heart rhythm disorder may refer to a clinically diagnosedcondition such as arrhythmias or any heart rhythm irregularities thatmay or may not have been formally diagnosed. The subject 105 may also bereferred to as a patient, a user, an individual, or a target individual.

A body surface device 110 is worn by or otherwise attached to thesubject 105. The body surface device 110 includes one or more sensorsthat detect biological signals of the subject 105 such as the heartrates and rhythm. Depending on the type of health condition, thebiological signals measured by the body surface device 110 may also bedifferent. In various embodiments, the body surface device 110 may takea different form, shape, and structure and include different types ofsensors. Non-limiting examples of the body surface devices 110 arediscussed in FIG. 2A through FIG. 3B. While the body surface device 110is described as a surface device, the body surface device 110 maygenerally be referred to as any non-invasive device that may or may notbe directly attached to the skin or another surface of the subject 105.The body surface device 110 may be network connected or may include awire port for connection with an electronic device (e.g., user device120 or another transceiver) for downloading and uploading of signal datacollected by the body surface device 110.

A catheter 115 may take the form of a conventional catheter well knownin the art or a specific ablation catheter equipped with one or more ofablation, sensing, and/or mapping capabilities. For example, in somecases, an ablation catheter may combine the functionality of sensingfrom multiple channels at high resolution, with therapy delivery(ablation) functionality into one tool. In such cases, the ablationcatheter may include a spade, a shaft, and a controller. The spade mayinclude an array of sensing electrodes for guiding the ablation catheterto one or more source regions. The spade may also include one or moreablation components for modifying the tissue region at a source regionof an arrhythmia. The spade may also include other components such asone or more irrigation pores for venting irrigant to tissue, one or morechambers for storing fluids such as coolant used for cryoablation, etc.The proximal end of the spade may be coupled to a shaft, which issteerable by a controller for controlling the movement of the spade. Insome cases, a shaft may further one or more contact sensors for sensingwhether the spade is in contact with tissue. Various types of sensorsmay be implemented as the contact sensor. In some cases, the contactsensor may take the form of a force sensor measuring a force applied tothe force sensor. The force sensor determines that the spade issufficiently in contact with the tissue surface when a force applied tothe force sensor is above a threshold, e.g., 0.25 Pascals. Another typeof sensor that may be implemented is a proximity sensor which senses adistance of another surface to the proximity sensor. The proximitysensor may measure the distance via capacitive sensing. A distance ofthe tissue surface to the proximity sensor affects capacitance of acapacitor implemented in the proximity sensor. The change in capacitanceis used to calculate the distance of the tissue surface to the capacitorin the proximity sensor. The proximity sensor may determine that thespade is sufficiently in contact with the tissue surface depending onthe distance of the tissue surface being within a threshold distance,e.g., 0.1 millimeters.

In some cases, the spade of a specific ablation catheter may alsoinclude an array of sensing electrodes that are placed on the contactsurface of the spade configured to come into the contact surface. Thesensing electrodes may be arranged in any suitable patterns, linear ornon-linear, regular or irregular, equally spaced or not, symmetrical ornot. For example, the sensing electrodes may be arranged evenly in arectangular grid. The size and spacing of the sensing electrodes maydetermine a resolution of sensing of the electrical signals. The sensingelectrodes detect electrical signals of a tissue. Other sensors can beplaced instead, to measure heat (infrared), mechanical motion(piezoelectric or other sensors), chemical composition or other indicesreferenced throughout the specification.

The ablation components of the catheter 115 may modify tissue withablation energy. The ablation components deliver ablation energy to thetissue or aid in delivery of the ablation energy to the tissue. Theablation components may include ablation electrodes that provideelectromagnetic energy as the ablation energy. The electromagneticenergy may include radio frequency electromagnetic waves, but may alsoinclude other frequencies of electromagnetic waves. In some cases, theablation components are cryoablation loci that provide freezing energyas the ablation energy.

In some cases, both detector and treatment elements may be included inthe same physical device, thereby eliminating the need to use separatetools for each. This reduces time and improves workflow, and may improveaccuracy since locations of desired target regions do not have to bestored or registered and then re-found using a separate tool.

The user device 120 is a computing device that is capable of receivinguser input as well as transmitting and/or receiving data via a network160. Example computing devices include desktop computers, laptopcomputers, personal digital assistants (PDAs), smartphones, tablets, orother suitable electronic devices. The user device 120 may be controlledby the subject 105 and may be the subject's smartphone. A user device120 communicates to other components via the network 160. In someembodiments, a user device 120 executes an application that launches agraphical user interface (GUI) 125 for a user of the user device 120 tointeract with the computing server 140. For example, the subject 105 mayview data illustration, alerts and other information generated from theanalysis of signals from the body surface device 110 and/or the catheter115.

The user interface 125 may be part of a software application provided bythe computing server 140 for the subject 105 to control the body surfacedevice 110 or to review data and information related to the body surfacedevice 110 and/or the catheter 115. For example, the user interface 125may be a patient-physician portal or an interface for a mobileapplication that pairs with the body surface device 110. The userinterface 125 may take various forms. The GUI may be an example of auser interface 125. A user device 120 may also execute a web browserapplication such as a web form to enable interactions between the userdevice 120 and the computing server 140 via the network 160. In anotherembodiment, the user interface 125 may take the form of a softwareapplication published by the computing server 140 and installed on theuser device 120. In yet another embodiment, a user device 120 interactswith the computing server 140 through an application programminginterface (API). The computing server 140 may provide the predictivebinding analysis as a software as a service (SaaS) platform through theinterface 125.

The physician 130 may provide both in person and remote consultation tothe subject 105 and may remotely and continuously monitor the conditionsof the subject 105 based on data and recommendations provided by thecomputing server 140, which may collect the signals generated by thebody surface device 110 and/or the catheter 115. The physician 130controls the physician device 132 that allows the physician 130 toreview data of the body surface device 110 and/or the catheter 115 andcommunicate with the subject 105 remotely through the interface 135. Thephysician device 132 and the interface 135 are respectively similar tothe user device 120 and interface 125. The examples and forms of thephysician device 132 and the interface 135 are not repeatedly discussed.

The computing server 140 may include one or more computing devices thatoperate one or more machine learning models 145 that may include one ormore predictive models that analyze the information provided by thesubject 105 and the physician 130 and data generated from the bodysurface device to generate recommendations such as therapyrecommendations and predictions related to the subject's conditions. Invarious embodiments, the computing server 140 may take different forms.The computing server 140 may be a server computer that includes softwareand one or more processors to execute code instructions to performvarious processes described herein. The computing server 140 may also bea pool of computing devices that may be located at the same geographicallocation (e.g., a server room) or be distributed geographically (e.g.,cloud computing, distributed computing, or in a virtual server network).The machine learning models 145 may be iteratively trained. Thealgorithms run by the computing server 140 may be used to identify arhythm disorder and direct treatments to the rhythm disorder. In someembodiments, the algorithms may take the form of software as a medicaldevice.

While in this example system environment 100 the computing server 140 isillustrated as a remote server, in various embodiments differentprocesses and software algorithm described in this disclosure (e.g.,processes described in FIG. 4 through FIG. 9C) may also be performed bya controller such as a computer that is attached to or in communicationwith the body surface device 110, the catheter 115, the user device 120,and/or the physician device 132. For example, in some embodiments, themachine learning model that is used to determine rhythm locations may beincluded in a local device at a point of care. Signal data generated bythe body surface device 110 or the catheter 115 does not always need tobe uploaded to the cloud.

The data store 150 may be one or more computing devices that includememories or other storage media for data related to the subject 105 suchas data generated from the body surface device 110. Some of the data maytake the form of personal digital records. The data may be routed by thecomputing server 140 and directly uploaded from the user device 120 orthe body surface device 110. The data store 150 may be a network-basedstorage server (e.g., a cloud server). The data store 150 may be part ofthe computing server 140 or may be a third-party storage system such asAMAZON AWS, AMAZON S3, DROPBOX, RACKSPACE CLOUD FILES, AZURE BLOBSTORAGE, GOOGLE CLOUD STORAGE or ENGINE, etc. In some cases, the datastore 150 also may be referred to as a cloud storage server 150.

The more detailed and broad the data included in personal digitalrecords, e.g., the “richer,” the data elements, the more comprehensiveis the digital classification (i.e. a classification) and the moreaccurate will be personalization of therapy. Personal digital recordscan input data from the electronic health record, such as heart rate,weight, other stored elements, and/or complex or sophisticated datawhich may change dynamically over time (e.g., proteomics and biomarkers)or may not change over time (e.g., genetic data). Other phenotypes maybe clinical labels not tracked by a biomarker, or those with loosestatistical definitions such as race or ethnic susceptibility.

Personal digital records can combine data from sensors, medical orconsumer machines alone or in combination. Data can be raw or firstmodified by signal processing. Data may come from specialized equipmentsuch as imaging systems or novel wearable sensors. Data may come frommultiple people for crowd-sourced population data. Data frompre-existing systems may include data from multiple hospitals in a largedigital registry of de-identified data, contributing diverse patients,practice patterns and outcome data from different therapies. Suchapproaches may involve blockchain technology to ensure data security,traceable logs of data transactions, and data access across multiplephysical storage systems.

Data received by the data store 150 may include data transmitted fromthe body surface device 110 and/or the catheter 115 and may also includeother data. Various data may take the form of sensed data streams.Sensed data streams may record from relevant tissue including the heart,nerves that supply regions of the heart, regions of the brain thatcontrol the nerves, blood vessels that supply regions of the heart, andtissues adjacent to the heart. For complex heart rhythm disorders,inflammation is a likely contributor that is often not included inphenotyping. Inflammation may cause some arrhythmias after surgery orother conditions such as myocarditis. The link of obesity with atrialfibrillation may operate through inflammation in pericardial fat, inturn, due to reactive oxygen species. Inflammatory findings may have asignificance that is undefined in any given person at one point orovertime, or between people. The “inflammasome” may measure the impactof inflammation from various pathological insults at the cellular ortissue level, yet is not commonly done, may not assess circadianfluctuations, have unclear relationships to inflammation for the entirebody, and may differ between individuals. It is thus unclear how toestablish “nomograms” of normal or abnormal states.

Biomarkers of inflammation can be a useful data stream. A personalizedstate of inflammation may be detected by inflammatory cells in theinflamed organ system, or in body fluids such as the blood, urine orcerebrospinal fluid. Byproducts of inflammation can be detected byelevated concentrations of biomarkers and cytokines such asinterleukin-6, nerve growth factor, matrix metalloproteinases.Conversely, several physiological markers are abnormal in inflammation(e.g., “acute phase reactants”). Inflammation causes, in addition toelevated white cell counts, abnormalities in red cell count, inhemoglobin concentration, and in a myriad of acute phase reactants suchas C-reactive protein, erythrocyte sedimentation rate or white cellcounts. In the heart, it is well known that serum troponin, a marker ofcardiac cell destruction, is an acute phase reactant whose levels fallwith inflammation (‘inverse acute phase reactant’).

In the subgroup of patients with inflammatory causes, arrhythmias may betreated by anti-inflammatory therapy including immunosuppression withagents such as tacrolimus, a hitherto unrecognized therapy for complexarrhythmias such as atrial fibrillation. Other immunosuppression therapysuch as steroids or non-steroidal agents, or cell therapy may beeffective. One rationale is that patients who receive heart transplantsrarely develop AF. While benefit is attributed to surgical isolation ofthe pulmonary veins during transplantation, PVI works in only 40-65% ofpatients in other populations. Another possible mechanism of AFsuppression in heart transplant patients is immunosuppressive agents.The use of immunosuppression for complex rhythm disorders including AFhas rarely been used. Digital taxonomies and personal digital records insome embodiments can identify individuals with inflammatory mediatedarrhythmias in whom anti-inflammatory therapy includingimmunosuppression may be useful.

For non-heart related applications, measurable body systems and sensedsignals include central and peripheral nervous systems, theelectroencephalogram (EEG) measured on the scalp, invasive electroderecordings or signals from peripheral nerves. Measurements may alsoinclude the respiratory system, skeletal muscles and skin, any indexesof electrical signals, hemodynamics, clinical factors, nerve signals,genetic profile, biomarkers of metabolic status, and patient movement.Other input data elements may come from imaging, nuclear, genetic,laboratory, or other sources, and may also be sensed as a stream (e.g.,transmitted to the system), or input as values at specific points intime.

In general, sensors may be in physical contact with the patient's bodywith the sensed data stream acquired by one of wired or wirelesstransmission. The sensor may be one or more of an electrode, an opticalsensor, a piezoelectric sensor, an acoustic sensor, an electricalresistance sensor, a thermal sensor, an accelerometer, a pressuresensor, a flow sensor, and an electrochemical sensor. Sensors may benon-contact, tracking physiological signals via emitted electromagneticradiation such as heat signatures (infrared), periodic alterations inskin reflectance that indicate heart rate (visible light), sonic signalsthat indicate breaths, and others evident to those skilled in the art.

Personalized therapy in an individual may include modifying at least aportion of tissue by one or more of ablation by energy delivery viacontact devices, energy delivery by noncontact devices, electricaltherapy, thermal therapy, mechanical therapy, delivery of drug therapy,delivery of immunosuppression, delivery of stem cell therapy, anddelivery of gene therapy.

Personalized therapy in an individual may further include guidingtherapy by another device. This may include guiding placement of apacing lead to the optimal site to stimulate the heart. This may includeguiding the selection of sites for cardiac resynchronization therapypacing. This may also include pacing sites that avoid pre-existing scarswhere signals are very small or attenuated.

In many cases, the personal digital record is then updated with personalhistorical data, the qualitative disease classifications, the actualintervention, its spatial location and other details, and its outcome.

The communications between the user devices 120, the physician device132, the computing server 140 and the data store 150 may be transmittedvia a network 160, for example, via the Internet. The network 160provides connections to the components of the system 100 through one ormore sub-networks, which may include any combination of local areaand/or wide area networks, using both wired and/or wirelesscommunication systems. In some embodiments, a network 160 uses standardcommunications technologies and/or protocols. For example, a network 160may include communication links using technologies such as Ethernet,802.11, worldwide interoperability for microwave access (WiMAX), 3G, 4G,Long Term Evolution (LTE), 5G, code division multiple access (CDMA),digital subscriber line (DSL), etc. Examples of network protocols usedfor communicating via the network 160 include multiprotocol labelswitching (MPLS), transmission control protocol/Internet protocol(TCP/IP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), and file transfer protocol (FTP). Data exchanged over anetwork 160 may be represented using any suitable format, such ashypertext markup language (HTML), extensible markup language (XML), orJSON. In some embodiments, all or some of the communication links of anetwork 160 may be encrypted using any suitable technique or techniquessuch as secure sockets layer (SSL), transport layer security (TLS),virtual private networks (VPNs), Internet Protocol security (IPsec),etc. The network 160 also includes links and packet switching networkssuch as the Internet.

The system environment 100 provides a novel process of a remote rhythmevaluation pathway that is enabled by the non-invasive body surfacedevice 110. By way of using heart rhythm disorder as an example, thebody surface device 110 identifies or predicts patients who will benefitfrom simple ablation, medications, or who may require complex surgery.For atrial fibrillation (AF), ablation therapy may include pulmonaryvein isolation (PVI) 101, medications 102 may include Dofetilide orSotalol (both of which require initiation in the hospital) or Mazesurgery 103. The computing server 140 analyzes the data from the bodysurface device 110. The recommendations from the computing server 140simplify and accelerate care without disrupting existing practicepatterns. In this embodiment, patients 105 with AF are seen by theirphysicians 130. The body surface device 110 may be delivered to apatient 105 such as by mail with online or in-person instructions foruse. The body surface device 110 is worn by the patient 105 to collectsufficient data, which may be hours, days or weeks. Data is analyzedlocally in a device or in the Cloud computing server 140 (or using acommercial computing engines such as AWS or Google Cloud) theninterpreted electronically by the physician 130. Alerts can be providedin a patient-friendly fashion by a dedicated device or on a smartphoneapp via interface 125. Data can be transmitted via electronic medicalrecords to the physician 130. The patient 105 may be sent directly to anappropriate specialist. This may be an electrophysiologist for PVIablation, an electrophysiologist or surgeon for advanced ablation beyondPVI, or a cardiologist for medications.

The novel remote rhythm evaluation process improves upon conventionalclinical workflow for managing patients with complex arrhythmias FIG. 1Bis a conceptual block diagram illustrating the conventional clinicalworkflow. Few tools currently exist to objectively guide the selectionof a drug or various types of ablation therapy in such patients.Diagnosis is often made by a general physician, who may refer thepatient to a cardiologist. The cardiologist may choose to startmedications or refer the patient for an invasive therapy such as anablation. Objective tools to guide this selection do not exist. As such,therapy often starts empirically with medication, the less invasive andinitially less costly approach. However, drug therapy often requiresin-hospital initiation, may fail over months or years, requiresprolonged follow-up and can be costly long term. Alternatively, personalpreference may guide referral for cardiac ablation, in which probes areadvanced from leg veins percutaneously to the heart to cauterize orfreeze regions of the heart related to the arrhythmia. Acutely, ablationis costly with some risk of complications, yet may be cost-effective inthe long term by eliminating arrhythmia in many patients. Nevertheless,it may fail in about half of patients, of whom some are referred formore invasive surgery. Ultimately, 20-25% of AF patients are resistantto all invasive therapies. A similar line of reasoning exists forpatients with ventricular arrhythmias, in whom about 50-60% may respondto ablation. This treatment workflow is lengthy, costly and exposespatients to the risks of procedures that may fail for them and havehealth and cost risks. This subjective approach may also perpetuateinequalities, for example, women and minorities are referred less oftenand later for ablation for unclear reasons. Tools are thus needed toobjectively identify optimal treatment pathways without subjective bias.

Example Body Surface Devices

FIGS. 2A and 2B are conceptual diagrams illustrating a non-invasive bodysurface device 110 for detecting one or more locations of the heart thatare associated with a heart rhythm disorder of a subject, in accordancewith one or more embodiments. Those locations may be referred to asrhythm locations (e.g., a heart rhythm location), which may includelocations of beats that initiate onset of a heart rhythm disorder andlocations of sources for a heart rhythm disorder. While detecting heartrhythm conditions is discussed throughout this disclosure as an exampleremote rhythm evaluation process, similar principles may also be appliedto other rhythm conditions in various embodiments. Sample non-invasiveembodiment of a body surface device 110 on the chest is shown withprojections from the right and left heart in FIG. 2A and pulmonary veinsto different regions of the torso in FIG. 2B.

In some embodiments, the body surface device 110 includes a substratecomprising one or more regions. Each region configured to be in contactwith one of the torso quadrants of the subject. The torso quadrants maybe a right anterior, a left anterior, a left posterior, and a rightposterior. In some embodiments, the substrate includes at least oneregion configured to be in contact with at least one of the torsoquadrants. The body surface device 110 may also include one or more setsof electrodes. Each set of electrodes is carried in one of the regionsof the substrate. The electrodes are configured to detect electricalsignals generated by the heart of the subject. In some embodiments, asubset of the plurality of electrodes is configured to measureelectromagnetic radiation including reflected light. The electrodes ineach set, which are carried in the region configured to be in contactwith the right anterior, the left anterior, the left posterior, or theright posterior, may be configured to detect the electrical signals fordetecting a heart rhythm disorder respectively from the left atrium, theright atrium, the left ventricle, or the right ventricle. In someembodiments, the electrodes configured to cover one or more spatialprojections of one or more areas of a heart projected on the bodysurface. In some embodiments, the electrodes configured to cover aspatial projection of at least a majority of a heart chamber projectedon the body surface. In some embodiments, the body surface device 110 isconfigured to record from an area of less than half of the torsosurface. In some embodiments, the body surface device 110 has an area ofabout or less than 100 cm². In some embodiments, the body surface devicerecords from a surface area of less than 200 cm².

In FIG. 2A, the body surface device 110 analyzes electrical activityfrom the heart rhythm disorder 200 in form of reentrant 205 or focalelectrical activity 210 or other patterns to identify the disorder'schamber of origin, which may involve analyzed metrics between bodysurface recordings in quadrants 220-235 or raw ECG recordings 215 for aplurality of beats. The patterns identified can be at the onset of aheart rhythm disorder, for instance to identify the location of triggerbeats for atrial fibrillation (AF). The patterns can also be identifiedduring heart rhythm disorder, such as to identify focal sources forfocal atrial tachycardia, focal ventricular tachycardia or reentrantcircuits for atrial flutter or for ventricular tachycardia. The analysisconcludes with a rhythm location identified in right anterior 220, leftanterior 225, left posterior 230 or right posterior 235, which maycorrespond to the arrhythmia origin in the left or right atrium, or leftor right ventricle. In FIG. 2B, the body surface device 110 analyzesbody surface regions selected to distinguish between left 260 and right265 pulmonary veins, other parts of the left atrium 270, and areas ofright atrium 275 such as superior cava vein 280, inferior cava vein 285or right atrial appendage 290. The body surface device 110 analyzes theheart rhythm disorder from the ECG signals on the torso to indicate itsregion of origin, which may be on the left back 295, right back 240,front left 245 or front right 250 and may indicate the origin of thearrhythmia from specific regions of the atrial anatomy. In someembodiments, the body surface device 110 does not guide a catheterwithin millimeters of a specific site for ablation, but identifiesspatial regions of interest where therapy may be effective. Ablation ofthis region should then be successful regardless if it is rotational,focal, repetitive of another configuration, low voltage or other.

In more details, the body surface device 110 is capable of identifyingelectrical activity patterns that includes centrifugal patterns,indicating single or repetitive focal activity (also termed a source),single or repetitive rotational patterns, which may indicate reentry orrotational activity or a ‘rotor’, other organized patterns which may besingle or repetitive, such as partial rotations, or no apparentorganization. In some embodiments, the body surface device 110 does notrequire the use of medical image data (CT or MRI scans) in order toperform this identification and analysis, although in some embodimentsthat data could be included in the analysis. For example, in someembodiments, a computing device, based on the signal data generated bythe body surface device 110, may related a location of an electricalactivity detected by the body surface device to a heart anatomy obtainedfrom imaging by one or more of magnetic resonance imaging, computedtomography imaging or echocardiography. In other embodiments, anatomicalinformation could be extracted from generic anatomic databases. Thesepatterns are identified in form of sequences of local activation times,as sequences of instantaneous phase analysis, by Poincare or recurrenceplots, by vectorial analysis or by other time-spatial analysis methodswhich may be familiar to one skilled in the art. This provides ananalysis of the triggering or initiating region for a heart rhythmdisorder, such as the first beats (focal beats with centrifugalemanation). If these sites lie repeatedly near the pulmonary veins andinitiate AF, then this may be a good site for pulmonary vein isolation(PVI) therapy in that patient. Alternatively, if sites that trigger AFarise from sites that do not lie near the pulmonary veins, then PVI maynot be the optimal therapy in this patient. In some embodiments, theanalysis is observational in the subject and the body surface device 110does not assume nor require specific biological mechanisms such as AFdrivers, AF sources, AF rotors, multiple wavelet reentry, multiple focalsources mechanisms related to fibrosis and so on.

FIG. 3A illustrates an example embodiment of a body surface device 110.

The designs in FIG. 3A are for full body torso recordings, but otherembodiments in FIG. 3B include devices designed to capture signals fromthe left versus right portions of the heart, or the pulmonary veinsversus other regions of the atria. In some embodiments, the body surfacedevice 110 may be large enough to cover the body torso projection of amajority of a heart chamber, including left or right atria, or left orright pulmonary vein antra, or left or right ventricles, or rightventricular outflow tract, or pulmonary artery or left ventricularoutflow tract or aorta. In some embodiments, the device will cover alower torso or abdominal projection to assess activity near the renalarteries which can be targets for ablation. The configuration ofelectrodes can be in square grids, so that electrical propagation can beassessed in any orientation, or in a zig-zag pattern (FIG. 3A) or in aseries of concentric circles or a spiral. Some of these patterns, suchas the concentric circle, may be well suited to examine centrifugalactivation from a focal sources, such as for focal atrial tachycardia orfocal ventricular tachycardia. The number of electrodes will vary withthe size of the body surface device and the biological application. Thebody surface device 110 in FIG. 3A includes tens of electrodesdistributed on the front 300, back 305 and side 310 of the torso mountedon flexible material 315 with adhesive. An alternative design uses anetched flexible circuit. Electrodes are configured in one or moreregions covering one or different parts of the torso. Patches containECG electrodes and may also contain reference electrodes for right arm320, left arm 325, right leg 330 and left leg 335.

In some embodiments, the device or patch (if it is deigned to recordfrom smaller regions than the entire torso) is constructed usingflexible material to conform to torso shape and size, and could be alsobuilt with removable or breakable material to enable better shapeadaptation 340. The thickness of the material may range fromsub-millimeter to 5 mm depending on electrode construction and location,since some portions of the body may need more durable material. Theconnection between electrodes and recording device could be made throughspecific connector including several wires or printed circuits 345.Alternative, or additionally, a body surface device 110 may include awireless transmitter (e.g., a WI-FI or BLUETOOTH transmitter) thattransmits readings from the body surface device 110 directly to acomputer.

FIG. 3B illustrates an example embodiment of a body surface device 110that is designed for focused regions of the torso. The body surfacedevice 110 uses a limited number of electrodes in a configuration thatwill cover regions of interest for different specific applications.Electrodes can cover different regions of the torso, with a densedistribution 350 for high resolution of specific heart regions or sparsedistribution 355 covering wider torso regions and reflecting activityfrom several heart chambers. Electrodes can cover single 360 or multiple365 torso regions depending on the heart disorder suspected to bediagnosed. For AF, regions of interest could be right or left atrium,and pulmonary veins versus other atrial regions. For generalarrhythmias, regions of interest will be right or left ventricle, orright or left atria. In some embodiment, the body surface device 110 maytake the form of one or more patches. The patches can be connected to anexternal storing device 370 and battery 375 or it can contain thestoring device 370 and battery 375. One or more device patches can beapplied in non-contiguous body regions, linked by wire 385 or wirelessly394 to a laptop 396, smartphone 392, computer 394 or another device.Patches contain ECG electrodes and may also contain reference electrodesfor the right arm, left arm and left leg, as shown in patch 380. In someembodiments, the body surface device 110 is constructed using flexiblematerial to conform to torso shape and size, and could be also builtwith removable or breakable material to enable better shape adaptation.The connection between electrodes and recording device could be madethrough specific connector including several wires or printed circuits.

The body surface device 110 may be used for diagnosing triggering sitesand source sites for electrical rhythm disorders to guide therapy. Thedevice is capable of sensing electrical signals and determining multiplesites that may be operative in that patient. The device may take theform of a patch. The patch is of sufficient size and appropriate shapeto encompass the signals that represent the heart rhythm disorder. Thesize, shape and location may differ for men and women. The patchcomprises an array of electrodes configured to detect a plurality ofelectrical signals generated by a heart and one or more other sensors. Acontroller is configured to determine the location of a trigger orsource region based on detected electrical signals detected by the arrayof electrodes. The controller is configured to locate these regionswithin the heart. The controller is further configured to instruct theoperator to guide therapy to the trigger, source or other target regionto treat the heart rhythm disorder. The body surface device 110 iswearable during daily activities.

Alternatively, or additionally, the body surface device 110 includesnon-electrical sensors. Example embodiments include electromagneticsensors for visible light, to provide photoplethysmography assessment ofperiodic fluctuations in blood flow, oxygenation or other composition.Some embodiments can sense near-infrared or infrared signals to identifyblood flow or other thermal signatures of heart physiology. Theseembodiments may be useful for applications in the head, such as toidentify increased blood flow over a seizure focus or tumor area. Someembodiments use this device with acoustic sensors to identify heartsounds which could be normal, elevated or reduced during heart rhythmdisorders, or elevated in heart failure. Some additional heart soundssuch as a third heart sound could be sensed in heart failure. Someembodiments use this device to listen to and quantify lung sounds frombreathing. The device could be used to identify absence of sounds fromsleep apnea or obstruction. The device may also be useful for assessinglung sounds during recovery from or worsening asthma, bronchitis orpneumonia. Such lung diseases could be caused by pollution such as fromfires or industrial or automobile sources, or from infections includingCOVID19. Specific patterns of lung sound abnormality can be identifiedin each, which will be apparent to those skilled in the art. In someembodiments, a device with acoustic sensors could be used on the abdomento sense bowel activity in patients with paralytic bowel after surgery(ileus), or with hyperactive bowel activity such as during irritablebowel syndrome or acute obstruction.

Example Signal Processing Pipeline

FIG. 4 is a conceptual diagram illustrating a mathematical body surfacemapping method to enable electrical pathways on the heart to bevisualized on the body surface using signals from a body surface device110, in accordance with one or more embodiments. FIG. 4 is a graphicalillustration of a data processing pipeline that may be performed bycomputing server 140 or any computing device for analyzing datacollected from a body surface device 110. In some embodiments, the bodysurface device 110 does not require the use of medical image data (CT orMRI scans) in order to perform the cardiac electric characterization,although in some embodiments that data could be included in theanalysis, extracted from the patient-specific MRI or CT scan orextracted from generic anatomic databases. The body surface device 110provides sufficient precision to visualize whether target areas forablation harbor critical areas for the arrhythmia. This may includedetecting if these areas lie in versus right heart or, if in the atria,near the pulmonary veins or not. The body surface device 110 can providethis detection based only on the analysis of the surfaceelectrocardiographic signals. FIG. 4 illustrates a full body torsoembodiment, but this process may also be applied to a device examiningsmaller torso regions (e.g., a device shown in FIG. 3B). In someembodiments, the smaller region is large enough to cover the projectionof the pulmonary veins onto the chest, such as for use in patients withatrial fibrillation in order to guide therapy by pulmonary veinisolation. In some embodiments, the smaller region is large enough tocover the projection of at least one heart chamber onto the body. Thatheart chamber is typically the chamber of origin of the heart rhythmdisorder. One example of this is a focal atrial tachycardia from theright or left atrium, that may require specialized therapy that is quitedifferent from therapy for AF or other complex arrhythmias Identifyingthat the source of the arrhythmia lies in the right atrium can eliminatethe need for left atrial access via trans-septal cannulation or otherprocedures. Conversely, identification that the source lies in the leftatrium can identify this need, so that the operator can plan for thesecomponents and a more lengthy procedure. Identifying the chamber oforigin of an arrhythmia ahead of time is currently poor from the priorart. The electrocardiographic signals 400 from the patches electrodes ofthe body surface device 110 may be recorded and mathematicallyprocessed. One or more of these ECG signals may be processedindividually or collectively. The number of electrocardiographic signalsneeded and their distribution on the torso surface may depend on thespecific heart rhythm being studied or the chamber that can be mapped.In some embodiments, raw electrocardiographic signals with no filteringor the electrocardiographic signals after band-pass filtering or othertypes of filtering 405 may be used.

In some embodiments, filtering 405 may include high-pass filtering above0.5 Hz to remove baseline oscillation or other artifacts, but others canbe selected. In another embodiment, filtering 405 can include low-passfiltering to remove electrical noise or other artifacts. Filtering caninclude also narrow-band pass filtering at spectral band determined byfeatures of the signal under analysis or other signals. For instance,some important features of AF in the frequency domain can be identifiedin bands of 0-20 Hz, such as the frequency of the main or secondaryspectral contributions, their width and relative amplitude as well asthe relative spectral content for certain frequency bands compared tothe total spectral content. These features could be considered whenselecting filters for signal acquisition. An embodiment could also useventricular activity cancellation when the aim is to identify originregions from the atrial chamber. In some embodiments, the ventricularcancellation algorithm is based on detection of the instant ofventricular depolarization using a combination of linear and non-linearfiltering and identification of local maxima. The ventricularcancellation algorithm could be based on ventricular shape average andsubtraction using one or more torso signals. The ventricularcancellation algorithm could be based on partial component analysisusing different ventricular beats.

The computing server 140 may perform spectral analysis of the torsosignals, using the Fast Fourier Transform 410, the Welch Periodogram,convolutional-based transform or the continuous wavelet transform. Thespectral analysis could be also based on the combination of spectraltransformations after different linear or non-linear filtering, such asband-pass filtering or Bottteron and Smith filtering. The spectralanalysis could be used to detect the main spectral contribution 415using the following formula:

DF=ϑ(s _(EcG))|_(ϑ(s) _(ECG) )=max(∥ϑ(s _(ECG))∥)

In the above equation, DF is the main spectral contribution or DominantFrequency, s_(EcG) is the surface signal under analysis and ϑ(s_(EcG))represents the spectral transform by Fast Fourier Transform or WelchPeriodogram. The computing server 140 may perform identification orother secondary spectral contribution using the local maxima of thespectral transform. The computing server 140 may perform the analysis ofthe spatial distribution of the DF values over the torso 420 in order toidentify regions with the same or different values of DF. The computingserver 140 may perform analysis of the phase of the surface signal 425,using the following or other formula:

phase(t)=arctan(imag(hilbert(s _(EcG)(t))),hilbert(s _(ECG)(t)))

In the above equation, phase(t) is the instantaneous phase transform ofthe signal under analysis s_(ECG), and imag( ) and hilbert( ) representsthe imaginary-part extraction and Hilbert transform functionsrespectively. The computing server 140 may perform the analysis of thephase from individual signals, by identifying the fiducial points suchas local maxima or transitions from/to pi/−pi. The computing server 140may perform the analysis of several instantaneous phase signals inspatial maps 430, using spatial interpolation of the phase signal ineach instant and position to cover all the surface torso betweenelectrodes 435. This spatial interpolation could be carried out usinglinear interpolation, cubic splines or other interpolation methods, andcould be carried out without the use of torso anatomies and shapesextracted from medical image (MRI, CT) techniques. The computing server140 may perform the analysis of the instantaneous phase maps through theidentification of the phase transitions, that is, the lines in which thephase map transits from pi to −pi. The computing server 140 may performthe analysis of spatial phase singularities using the following formula:

singularity(t)=

_(,D) ^(2π)phase(s _(EcG)(t)_(x,y))

In the above question, the operator

_(0,D) ^(2π) represents the spatial integral over a circle with radius Dand s_(EcG) (t)_(x,y) is the electrocardiographic signal at interpolatedcoordinates X and Y. The computing server 140 may perform identificationof instants and points in which the singularity(t) provides valuesdifferent to 0 and summarize and cluster them to measure the spatial andtemporal complexity of heart arrhythmia. The computing server 140 mayperform the analysis of the temporal features of theelectrocardiographic surface signal as the number of local maximal afterband-pass filtering 440. The computing server 140 may perform theanalysis of the first and second derivatives of the torso surface signal445 in order to identify their percentiles and quartiles 450. Thecomputing server 140 may perform autocorrelation analysis of theelectrocardiographic surface signals 455.

In some embodiments, the computing server 140 may identify focal beats,such as from rapidly firing regions near a pulmonary vein. Focal beatscould be identified in body surface potentials and/or their derivatives,characterizing atrial or ventricular complexes with specific traces thatrepresent a focal beat origin. When analyzing a series of signals, asequence of activation emanating outwards from a point would support afocal source. Other characteristics can be analyzed based on therelative temporal or spatial position of tracings, such as the frequencyof focal activation, the size and shape of the region activated thoughthe focal beat and the relative tridimensional (x,y,z) position andorientation of the focal site with respect to surface electrodes—inother words, does activation emanate from, to or parallel to the surfaceelectrodes. Focal beats could also be identified using the phasetransform applied to multiple electrocardiographic surface signals.Focal sources could be identified as regions and instants with expandingcircles of constant phase values or with phase values different than thesurrounding phase map points. Focal beats could be also identified usingspatial and temporal derivatives of the electrocardiographic signals,such as the divergence, in order to identify regions with positivedivergence values or sites in which the spatial and temporal derivativesindicates emanating potential sources.

In some embodiments, the computing server 140 may also identifyrepetitive activations which do not exhibit a focal or reentrantpattern, which has been proposed to drive some arrhythmias including AF.Identifying repetitive activations may be performed usingspatio-temporal analysis such as Granger causality betweenelectrocardiographic signals, in which strong causal relations betweenpairs of signals can be characterized and summarized in maps. Such mapscan be then interpreted to identify emanating (outward) patterns, thatis, regions from which the causal relations emerge, or as regions inwhich causal relations are reentrant, using divergence or rotationalvectorial metrics or other techniques to analyze vectorial maps.Repetitive activations could be also identified in phase mapsconstructed from different electrocardiographic signals as singularitypoints, that is, regions and instants in which the phase map reflectsincreasing and circular distribution on phase values. Repetitiveactivations can also be identified in single or multipleelectrocardiographic signals by the analysis of the potential seriessignals and/or their derivatives, characterizing specific atrial orventricular complexes that present specific and repetitive signaltraces. Finally, repetitive activation can be identified usingcorrelation analysis of specific combinations of ECG signals atdifferent locations over time. Analysis of repetitive activations couldbe carried out without the use of the torso anatomy and shape extractedfrom medical image techniques (MR, CT).

In some embodiments, the computing server 140 may calculate a cardiacoutput and determine whether the cardiac output is reduced. In response,the computing server 140 may send an alert that the cardiac output isreduced.

Example Process for the Personalized Classification of Rhythm Location

FIG. 5A is a conceptual diagram illustrating an algorithm process toclassify rhythm locations from the body surface recording signalsgenerated by a body surface device 110, in accordance with one or moreembodiments. FIG. 5A is a graphical illustration of an inferencealgorithm that may be performed by computing server 140. The embodimentshows the development of signatures of a rhythm disorder which can beused to classify the rhythm, or identify special regions and/or specialtimes within the rhythm disorder. The signal data used in FIG. 5A may bea version of signals generated by the body surface device, such as theraw signals or signals that are processed by the pipeline illustrated inFIG. 4. The process in FIG. 5A may be used to identify the location ofrhythm to classify right or left atrial or right or left ventricularorigin. This can be structured to separate pulmonary vein ornon-pulmonary vein regions for atrial fibrillation one embodiment.Similar algorithmic processes may be for other types of rhythm disordersthat are not related to hearts, such as for seizure disorder in thebrain, activity in the gastrointestinal tract, or nerve firing in aportion of the body in neurological illness.

Classification can either be based on a combination of raw voltage-timeseries data 505 and features of the raw voltage-time series 510.Featurization of data can be used to separate supraventricular andventricular arrhythmias, and their chamber of origin. This featurizationcould include spectral and phase analysis in individual and collectivesurface signals, as well as other features extracted from the temporalsignal domain as the number of local maxima, cycle length, percentiles,amplitudes, variance, autocorrelation measures or entropy, among others.With respect to rate 515, in some embodiments, basic rules are used toseparate tachycardias (rate >100/min), isolated premature beats (atrialor ventricular; isolated rate >100/min) or bradycardia (rate <60/min).These categories may be used to automatically separate beat categoriesfor analysis. This automatic separation may be carried out usingfeatures extracted from the surface signals such as autocorrelation,dominant frequency, cycle length or other methods. Atrial versusventricular activity are separated by established rules, to separatesupraventricular from ventricular tachycardias. Secondary analyses areperformed for the ventricular and the atrial electrical activity.

Directional information from activity maps and others signal andfeatures are collected within or between quadrants of the torso. Thisfeature separates left-to-right versus right-to-left vectors, and alsoanterior-to-posterior versus posterior-to-anterior vectors.Directionality is derived mathematically from apparent conductionvelocity at each body surface electrode as path length (inter-electrodedistance) divided by activation time, identified in the instantaneousphase or as local maxima or minima. The activation time field representsthe projection of the wavefront velocity vector on the torso and allowsto identify the propagation direction as the maximal gradient directionon the activation field with the following formula:

${directio{n_{x,y}(t)}} = \left( {\frac{{\partial a}c{t\left( {t,x,y} \right)}}{\partial x},\frac{{\partial a}c{t\left( {t,x,y} \right)}}{\partial y}} \right)$

where act(t, x, y) represents the activation field function in space(x,y) and time (t) and direction_(x,y)(t) is the gradient of the vectorfield at positions X and Y for the instant t. The path of slowestconduction is in the direction of the outward unit normal of theadvancing wavefront, and points outwards the initiating region.

As shown in FIG. 5A, the computing server 140 may use machine learningor statistical models 520 to analyze data from the voltage-time series,any of the extracted features, as well as clinical and demographic andother information from the patient 105 under study. The models aredesigned to provide an estimate of the origin of the arrhythmia whichcan be left or right side of the heart or ventricles/atrium 525. Thisclassifier can be also be used to identify the best subsets ofelectrodes able to identify the origin of the arrhythmia with a numberof electrodes 530 based on an analysis of the Receiver Operating Curves,accuracy or other coincidence metrics of different electrodeconfigurations 535.

One or more supervised machine learning and statistical methods can beused to predict the arrhythmia origin including but not limited toneural networks, convolutional neural networks, recurrent neuralnetworks, support vector machines, decision trees, discriminantanalysis, naive bayes, and others. The input to the machine learningalgorithms can be the voltage time-series data or features derived fromthe raw voltage time-series such as the aforementioned features. Theoutput of the machine learning algorithms can be two-class (binary),multi-class, univariate, multivariate, or a combination of differentoutput types. Unsupervised learning algorithms can also be used tofeaturize and cluster similar data together in case labels are missing.Unsupervised algorithms include k-means, principal component analysis,singular value decomposition, autoencoders, or other methods.Semi-supervised machine learning algorithms, which combine concepts fromboth supervised and unsupervised learning, can also be used when somedata is missing labels. In semi-supervised algorithms, labeled data areused to pseudo-label unlabeled data and to improve the machine learningperformance. Once a machine learning model is trained, the model isprobed 535 to better understand what types of data inputs are mostimportant for each classification or prediction.

In some embodiments, the computing server 140 or local device (e.g.Physician device 132 or other computing device) may use one or moreexplainability (or interpretability) techniques. Local InterpretableModel-agnostic Explanations (LIME) can be used to explain predictions byaltering the input and observing how the output changes. LIME can beused for 1-dimensional data such as the ECG or electrical signals fromwithin the heart (electrograms), numeric features or images. SHAP(Shapely Additive exPlanations) uses concepts from Cooperative GameTheory and local explanations, where an input or a feature is replacedby a random value from the data and the difference in predicted outputis measured. Another approach is Gradient-weighted Class ActivationMapping (Grad-CAM), which identifies the most critical nodes as thelargest output weights multiplied by output's backpropagated gradientswith respect to the final convolutional layer.

In one embodiment, the system uses explainability tools to identify theoptimal leads 535. One of the methods above, such as LIME or Shapleyvalue can be used to indicate which portions of the input data set(input vector) are most important to the classification of heartlocation, and hence which electrodes are the most important and shouldbe part of the recording patch on a portion of the body torso. This canbe personalized for men versus women, for persons with different bodytorso shapes such as extreme obesity or very tall individuals, and evenfor a specific individual.

This latter individualization of torso lead positioning can be used totrack an arrhythmia over repeated recordings, and identify if instancesrepresent the same or a different rhythm. This is particularly usefulfor conditions such as atrial fibrillation, atypical atrial flutter,focal atrial tachycardia, focal ventricular tachycardia or reentrantventricular tachycardia. In these conditions, it is often unclear ifclinical episodes represent the same arrhythmia or other arrhythmiaspotentially from different locations. This has great impact over theapproach to therapy.

In another embodiment, analyses specify features that should or shouldnot be part of the model including spatial domains in images (e.g., sizeof an atrial driver region, or ventricular conduction velocity, orspatial extent of fibrosis in the human heart) enabling tailoredinterpretation to domain electrophysiological “concepts” to ensure thatmodels do not converge on irrelevant concepts. An example of this is theTesting with Concept Activation Vectors (TCAV) approach. This canexamine specific features that should or should not be part of the model(e.g., size of AF driver regions), enabling the computing server 140 totest explainability analyses to accepted “concepts” and thus ensure thatthe solution is realistic and plausible. As another example, theprediction of an AF outcome (e.g., success or failure of ablation) canbe tested by an interpretable model, e.g., presence of fibrosis near theright atrium. This approach attempts to ensure that numerical models arerelevant to predictions, and models do not converge on irrelevantconcepts. Explainable features predicting outcome will thus beidentified quantitatively. The clinical rationale can subsequently beadded via domain knowledge, e.g., the determination that obesitypredicts negative outcomes from ablation or drug therapy, while haircolor predicting positive outcomes may not. Data on populations in whomclass IC anti-arrhythmic drug (AAD) may be used can also be included.

In some embodiments, machine learning models 520 receive data from thebody surface device 110, which may take the form of non-invasive ECGpatches. The data may be raw or featured. The machine learning model 520maps the data to the anatomical location in the heart as well aspredicting a ranked list of therapies showing which is most likely tobenefit the patient 105. This prediction can be performed without theuse of patient-specific anatomies extracted from medical imagetechniques (MRI, CT). The model can also utilize other data streams thatwere recorded from other sensors or databases. The input data streamscan be used in their raw type, preprocessed, or featurized to improvemodel predictions.

In various embodiments, a wide variety of machine learning techniquesmay be used. Examples include different forms of supervised learning,unsupervised learning, and semi-supervised learning such as decisiontrees, support vector machines (SVMs), linear regression, logisticregression, Bayesian networks, and genetic algorithms Deep learningtechniques such as neural networks, including convolutional neuralnetworks (CNN), recurrent neural networks (RNN), long short-term memorynetworks (LSTM), and auto-encoders may also be used. For example, themachine learning model 520 shown in FIG. 5A may apply one or moremachine learning and deep learning techniques.

In various embodiments, the training techniques for a machine learningmodel may be supervised, semi-supervised, or unsupervised. In supervisedlearning, the machine learning models may be trained with a set oftraining samples that are labeled. For example, for a machine learningmodel trained to the known rhythm location based on sensor signals. Thelabels for each training sample may be binary or multi-class. Intraining a machine learning model for identifying rhythm location, thetraining samples may be signals of patients diagnosed with known rhythmdisorders at known locations. The label may be the rhythm locations ofthose patients. In another embodiment, the label may be the type ofrhythm condition, to differentiate atypical atrial flutter from atrialfibrillation, for instance. In some cases, an unsupervised learningtechnique may be used to identify samples which are similar to eachother and hence those that are different. The samples used are notlabeled. For example, patient data without determination of the actualrhythm locations may be used in unsupervised learning. Variousunsupervised learning techniques such as clustering (k-means and otherclustering techniques) may be used. In some cases, the training may besemi-supervised with the training set having a mix of labeled samplesand unlabeled samples.

A machine learning model may be associated with an objective function,which generates a metric value that describes the objective goal of thetraining process. For example, the training may intend to reduce theerror rate of the model in predicting the rhythm locations. In such acase, the objective function may monitor the error rate of the machinelearning model. Such an objective function may be called a lossfunction. Other forms of objective functions may also be used,particularly for unsupervised learning models whose error rates are noteasily determined due to the lack of labels. In the prediction of rhythmlocations, the objective function may correspond to the differencebetween the model's predicted rhythm locations and the manuallydiagnosed rhythm locations in the training sets. In various embodiments,the error rate may be measured as binary or categorical cross-entropyloss, L1 loss (e.g., the sum of absolute differences between thepredicted values and the actual value), L2 loss (e.g., the sum ofsquared distances), or others. A combination of loss functions may beused in one machine learning model. L1 and L2 may also be used asregularization techniques as well to prevent overfitting.

FIG. 5B is a diagram illustrating an algorithm process to extractspecific rhythm signatures in the body surface and/or intracardiacsignals. The embodiment shows the development of signatures of a rhythmdisorder which can be used to classify the rhythm, or identify specialregions and/or special times within the rhythm disorder. These specialtimes and/or regions can be treatment targets. FIG. 5B illustratesfeature identification and classification that may be performed bycomputing server 140. The signal data used in FIG. 5B may be a versionof signals generated by the body surface device, such as the raw signalsor signals that are processed by the pipeline illustrated in FIG. 4, ormay be signals recorded by intracardiac catheters.

FIG. 5B also shows reconstructed signals and an algorithm that isapplied to these specific signals to create fingerprints or footprintsor signatures of the rhythm. The signature may classify the heart rhythmdisorder, such as atrial fibrillation or atrial tachycardia or atrialflutter or ventricular tachycardia and so on. The process in FIG. 5B maybe used to refine the identification of the location of rhythm toclassify right or left atrial or right or left ventricular origin. Thiscan be structured to identify pulmonary vein from non-pulmonary veinregions for different embodiments. This can be useful to separateconditions such as atrial flutter from fibrillation, which guidestherapy. This can also be useful to separate different forms of atrialfibrillation, such as those which can be treated by pulmonary veinisolation compared to forms that require therapy at additional areasoutside the pulmonary veins. Similar algorithmic processes may be forother types of rhythm disorders that are not related to hearts, such asfor seizure disorder in the brain, activity in the gastrointestinaltract, or nerve firing in a portion of the body in neurological illness.

The signature may also identify a signal type that is a treatment targetfor the heart rhythm disorder, such as a region of slow conduction, of aviable channel of tissue within scar, or fractionated signals, of highrates, of source or driver activity and so on.

The signal signature may or may not be clear from analyses of thetime-domain characteristics of the signal, such as amplitude, rate orshape. The signal signature may or may not be clear from analyses of thefrequency domain characteristics of the signal, such as frequency,harmonics or phase. The signature may extend to signals from neighboringelectrodes to form a preferred spatial region or cluster.

Data acquired from surface electrodes 552 provide raw signals or signalsthat are processed by the pipeline illustrated in FIG. 4, as well asintracardiac electrical recordings from multipolar catheters 554, couldbe used individually or in conjunction giving signals 556 to trainclassifiers able to identify the rhythm origin 566 or to predict theablation success in a specific patient 568. These classifiers could usea variety of input data to perform the classification of the raw orprocessed signals 558, or features extracted from these signals 560 asexplained in FIG. SA, or patient demographics 562 such as sex or age.

For a catheter within the heart 554, contact can be enhanced using avariety of compliant materials, depending on the intended locationwithin the organ of interest. One type of catheter uses a conformablechamber filled with cryo-solvent for mapping and ultimatelycryoablation, in which the therapy device adheres to tissue duringenergy delivery for rapid, accurate and safe ablation. This can beeffective to ablate sources of AF and atrial tachycardias in the heart,and seizure foci in the brain. One embodiment uses a nitinol frame uponwhich electrodes are mounted. The device thickness should be sufficientto support the array of electrodes against the contours of the tissue,while being flexible enough to be collapsed and folded into a sheath. Anexemplary thickness range would be on the order of 0.10-4.0 mm but mayvary depending on the components and features incorporated into thedevice. In some embodiments, a range of 0.75-1.0 mm will be flexibleenough to conform to the heart chamber while providing enough supportfor the electrode material. In another embodiment, a range of 2-3 mmwill provide greater structural stability for use outside the heart,such as for cardiac surgical applications, or for the ventricle whichhas a greater range of contractile motion.

In order to refine the classifier 564 performance, reconstructedrecordings 570 from the body surface or intracardiac catheters could beused. These reconstructed recordings could be body surface orintracardiac signals in which a specific characteristic is changed andvaried. For instance, reconstructed recordings could be obtained byreconstructing body surface or intracardiac signals with varying shapeor rate. Reconstructed recordings could be processed in the computerserver 140. These reconstructed signals would compose a database inwhich one or more of these parameters is changed at a time, keeping allthe rest as in the departing body surface or intracardiac signals.Reconstructed signals 570 could be then classified using the trainedclassifier 572 described in FIG. 5B in order to obtain theclassification labels 574 for each of these reconstructed signals. Theseclassification metrics 574 on reconstructed signals 570 could be thenused to identify the response of the trained classifier 572 to eachparameter used in the reconstruction 576 and the relationship betweenthe parameter under study in the reconstructed signal and the rhythmclassification or other classification under study. This parametricinformation 576 could be then used to refine the classifier 564 or toidentify new features 560 used in the classifier. Classification metrics574 on reconstructed signals 570 could be used to identify specificsignal traces or signatures 578 that are specific for certain rhythms ordiseases or other classification problems under study. These signalsignatures 578 identified in body surface or intracardiac data can beused to refine the classification performance 564 by the identificationof these signatures in signals under study using convolution,correlation or other metrics. Signatures 578 can be used to identifynovel features 560 used by the classifier 564.

FIG. 5C are graphical illustrations of examples of rhythm parameters andsignatures identified in body surface or intracardiac signals. FIG. 5Cis a graphical illustration of parameters and signatures identificationthat may be performed by computing server 140 or a local device. Thesignal data used in FIG. 5C may be a version of signals reconstructedusing body surface device, such as the raw signals, signals that areprocessed by the pipeline illustrated in FIG. 4, or signals recorded byintracardiac catheters. The examples in FIG. 5C may be used to refinethe classifier of the location of rhythm to classify right or leftatrial or right or left ventricular origin. This can be structured toidentify pulmonary vein from non-pulmonary vein regions for differentembodiments. Similar algorithmic processes may be for other types ofrhythm disorders that are not related to hearts, such as for seizuredisorder in the brain, activity in the gastrointestinal tract, or nervefiring in a portion of the body in neurological illness.

Parametric studies can be carried out using reconstructed signals 570.This can evaluate the performance of shape regularity 582 onclassification, evaluated through reconstructed signals in which shaperegularity is varied by replacing individual beats 584 in differentproportions of each reconstructed signal. The predicted label for eachreconstructed signal 586 classifies them into regular (such as AtrialFlutter) or irregular (such as Atrial Fibrillation) rhythms. This canidentify the range of shape regularity which the trained classifier usesto perform classification. A different parametric study could be carriedout to assess the performance of rate 588 on classification. Here,reconstructed signals have varying rate 590 for each reconstructedsignal. The predicted label for each reconstructed signal 592 classifiesthem into regular (Atrial Flutter) or irregular (Atrial Fibrillation)rhythms, to identify the range of rate that the trained classifier usesto perform classification.

Specific signal signatures for each rhythms or other diseases 594 can beidentified using reconstructed signals in which a single beat shape 595is used repeatedly to reconstruct signals with regular shape butdifferent rates and timing regularity 596. In some embodiments,classification 597 of the dataset of reconstructed signals with uniquebeat shape is used to identify those beat shapes 595 whose reconstructedsignals had a predominant classification into one of the possible labelsof the classifier. These individual beats 595 whose reconstructed signalpresent a predominant classification could be used as body surface orintracardiac electrogram signatures to refine the classificationperformance as described in FIG. 5B. In other embodiments,classification 597 of the dataset of reconstructed signals with uniquebeat shapes is used to identify specific regions and times for therapy.This may include targeting ablation to the site of that electrogramsignature from beat shapes 595 or other parameters.

Referring to FIG. 6, a structure of an example neural network isillustrated, in accordance with one or more embodiments. The neuralnetwork 600 may receive inputs 610 and generate an output 620. Whileinputs 610 is graphically illustrated as having two dimensions in FIG.6, the inputs 610 may be in any dimension. For example, the neuralnetwork 600 may be a one-dimensional convolutional network.

The neural network 600 may include different kinds of layers, such asconvolutional layers 630, pooling layers 640, recurrent layers 650, fullconnected layers 660, and custom layers 670. A convolutional layer 630convolves the input of the layer (e.g., a matrix of any dimension) withone or more weight kernels to generate different types of sequences thatare filtered by the kernels to generate feature spaces. Each convolutionresult may be associated with an activation function. A convolutionallayer 630 may be followed by a pooling layer 640 that selects themaximum value (max pooling) or average value (average pooling) from theportion of the input covered by the kernel size. The pooling layer 640reduces the spatial size of the extracted features. In some embodiments,a pair of convolutional layer 630 and pooling layer 640 may be followedby a recurrent layer 650 that includes one or more feedback loops 655.The feedback 655 may be used to emphasize or account for spatialrelationships of the features in an image or temporal relationships insequences. The layers 630, 640, and 650 may be followed in multiplefully connected layers 660 that have nodes (represented by squares inFIG. 6) connected to each other. The fully connected layers 660 may beused for classification and object detection. In some embodiments, oneor more custom layers 670 may also be presented for the generation of aspecific format of output 620. For example, a custom layer may be usedfor image segmentation for labeling pixels of an image input withdifferent segment labels.

The order of layers and the number of layers of the neural network 600in FIG. 6 is for example only. In various embodiments, a neural network600 includes one or more convolutional layer 630 but may or may notinclude any pooling layer 640 or recurrent layer 650. If a pooling layer640 is present, not all convolutional layers 630 are always followed bya pooling layer 640. A recurrent layer may also be positioneddifferently at other locations of the neural network. For eachconvolutional layer 630, the sizes of kernels (e.g., 1×1, 1×2, 3×3, 5×5,7×7, N×M, where N or M=1,2,3, . . . , etc.) and the numbers of kernelsallowed to be learned may be different from other convolutional layers630.

A machine learning model may include certain layers, nodes, kernelsand/or coefficients. Training of the neural network 600 may includeforward propagation and backpropagation. Each layer in a neural networkmay include one or more nodes, which may be fully or partially connectedto other nodes in adjacent layers. In forward propagation, the neuralnetwork performs the computation in the forward direction based onoutputs of a preceding layer. The operation of a node may be defined byone or more functions. The functions that define the operation of a nodemay include various computation operations such as convolution of datawith one or more kernels, pooling, recurrent loop in RNN, various gatesin LSTM, etc. The functions may also include an activation function thatadjusts the weight of the output of the node. Nodes in different layersmay be associated with different functions.

Each of the functions in the neural network may be associated withdifferent coefficients (e.g. weights and kernel coefficients) that areadjustable during training. In addition, some of the nodes in a neuralnetwork may also be associated with an activation function that decidesthe weight of the output of the node in forward propagation. Commonactivation functions may include step functions, linear functions,sigmoid functions, hyperbolic tangent functions (tan h), and rectifiedlinear unit functions (ReLU). After input is provided into the neuralnetwork and passes through a neural network in the forward direction,the results may be compared to the training labels or other values inthe training set to determine the neural network's performance. Theprocess of prediction may be repeated for other inputs in the trainingsets to compute the value of the objective function in a particulartraining round. In turn, the neural network performs backpropagation byusing gradient descent such as stochastic gradient descent (SGD) orother optimization techniques to adjust the coefficients in variousfunctions to improve the value of the objective function.

Multiple rounds of forward propagation and backpropagation may beperformed to iteratively train a machine learning model. Training may becompleted when the objective function has become sufficiently stable(e.g., the machine learning model has converged) or after apredetermined number of rounds for a particular set of training samples.The trained machine learning model can be used for performing variousmachine learning tasks as discussed in this disclosure. While thestructure of a neural network is illustrated in FIG. 6, various othertypes of machine learning models, such as support vector machines,gradient boosted trees, random forests, may also be used in differentprediction and analysis pipelines in this disclosure. The trainingtechniques discussed in FIG. 6 may also be applied to those algorithms

Example Therapy Recommendation Process

FIG. 7 is a flowchart depicting an example process that is executable bysoftware algorithms for a computing system (e.g., computing server 140)to provide one or more arrhythmia management recommendations based ondata collected by a body surface device 110, an invasive catheter device115, or both, in accordance with one or more embodiments. The softwarealgorithm may be stored as computer instructions that are executable byone or more general processors (e.g., CPUs, GPUs). While computingserver 140 is used to describe the process, the process may be performedby any computing device. The instructions, when executed by theprocessors, cause the processors to perform various steps described inthe process. FIG. 7 illustrates one example to use patient data tomanage and treat ablation procedures for atrial fibrillation. Thepatient data may include data on the activity patterns of the patient,which can be obtained from non-invasive tool 700 such as the bodysurface device 110 and/or invasive tools 730. In some embodiments, thecomputing server 140 receives only data from the non-invasive tool, suchas the body surface device 110. Steps 700-740 are the first triage step,which identifies for a patient if empirical ablation will work. Steps750-770 personalize AF mapping, map interpretation and ablation. In someembodiments, the computing server 140 may compute a predicted successscore for a planned therapy for eliminating one or more regions thatinitiate an onset of the heart rhythm disorder or regions that maintainthe heart rhythm disorder. For example, the planned therapy may beablation as discussed in step 710 or non-ablation therapy as discussedin step 720.

In step 700, the computing server 140 receives non-invasive signals forAF. In some embodiments, the signals may include body surface potentialmaps (or potentially ECG imaging, ECGI) which may use up to hundreds ofbody surface leads (e.g., 252 leads). The signals may be raw signals orsignals processed by the pipeline described in FIG. 4. In someembodiments, the body surface device 110 uses fewer ECG leads, as few as<20. The non-invasive inputs can also include the standard 12-lead ECG,a subset of the 12-lead ECG, magnetocardiography (MCG), non-invasivestructural imaging and other features that can be obtained prior to theinvasive study.

The process illustrated in FIG. 7 provides an option for diseaseprediction, in which the inventive technique identifies patient types(phenotypes) who do not manifest AF but who may be at risk for AF. Thismay be due to specific patterns of structural abnormality marked by lowvoltage or potentially abnormal on delayed enhanced magnetic resonanceimaging. In this case, the computing server 140 provides for AFprediction. The server 140 may also provide for prediction of risk forventricular tachycardia if activity is slowed in one region of theventricle or if erratic patterns consistent with conduction through scaror “late potentials” can be identified even in sinus rhythm. This may beparticularly useful in patients with prior structural heart diseaseincluding prior heart attack (myocardial infarction). This embodimentcould also be used in patients with different forms of structuraldisease including congenital heart disease, or heart valveabnormalities. In another embodiment, the system can identify ifactivation between left and right sides of the heart are synchronized.This can assess the effectiveness of cardiac resynchronization therapy,in which dys-synchronous regions of the heart are ‘resynchronized’ bystrategically tailored pacing leads. This embodiment would enablesynchronization therapy to be modified and titrated to optimize left andright synchronization. Although typically applied for the ventricle,left and right synchrony could also be assessed for the atria, to ensureoptimum flow of blood through the heart. Finally, the system in anotherembodiment can assess if the left atrial appendage is electricallyactive, since patients with inactive or reduced activity in the leftatrial appendage may be at risk for stroke. This may be related to clotformation in this structure, and may occur after prior surgery ortherapy to the heart or after a device has been placed. Input data inthis case may include granular imaging data showing MRI abnormalities,or granular data on regions of low voltage to enable non-invasivedetection of structural risk profiles by the network to provide aprognosis, or potentially targets for therapy. Treatment may includeablation to connect these regions of scar or fibrosis.

The computing server 140 may personalize the guidance of ablation. Instep 705, the computing server 140 identifies the expected or desiredablation targets. Many of the targets are already defined, althoughoften these targets provide modest success in the prior art. Forinstance, AF ablation is often performed using pulmonary vein isolation(PVI). This is done for patients with early stage AF as well as laterAF, but it is not known if this procedure will work in any one patientand the overall success is 40-60%. One embodiment would input PVI inthis step to determine if this approach will or will not work for agiven patient, e.g., to determine if that patient is in the 40-60%success group. This may be based on patients in whom this device showsinitiating trigger beats for AF near the pulmonary veins (PV). It mayalso be effective for patients in whom this device has shown importantactivity during AF (sources) including high rates or focal or reentrantactivity at the PVs. In other embodiments, regions of interest may bethe right atrial cavotricuspid isthmus, a common site of arrhythmias, orthe left atrial mitral annulus. For atrioventricular nodal reentry,common sites for ablation are the slow pathway position in the rightatrial septum. In the ventricle, common sites for ablation are the rightor left ventricular outflow tracts. Other targets will be familiar toone skilled in the art.

In step 710, the computing server 140 determines whether ablation islikely to work in this patient. This is done in some embodiments bycomparing non-invasive and invasive data (whichever is available) in thepatient data to a digital classification of how patients with similarpatterns responded to ablation. If the classification concludes thatsimilar patients did not respond to ablation, that is the conclusion instep 720.

The computing server 140 provides outputs 720 that are determinedquantitatively in an individual by the non-invasive or invasive data(whichever is available), the disease-specific personal digital record(here, for arrhythmia) and the digital classification. For the specificembodiment of AF therapy, outputs comprise ablation or non-ablationtherapy that may include drug therapy and lifestyle changes. Thecomputing server 140 may assign scores to each of these outputs usingsteps outlined in FIG. 5A, including demographic inputs 510 andreversible factors such as high body mass index, poorly treateddiabetes, sedentary lifestyle and excessive alcohol consumption, etc.Pharmacological (drug) therapy may be assigned a higher score in apatient of older age, without heart failure and with prior failed AFablations. These analyses include several other features which will beknown to those skilled in the art, to tailor recommendations by thepatient data and digital classification for AF. Conversely, if thedevice shows critical AF regions near the pulmonary veins or otherregions amenable to ablation, then ablation is assigned a higher score.If the device indicates no critical regions near PVs or in other regionsamenable to ablation, then ablation is assigned a lower score.

If step 710 identifies that ablation is likely to be successful in thispatient, then steps 740 onwards are engaged. The computing server 140determines if the regions of interest for the arrhythmia in this patientlie near proposed regions of ablation in step 705. In some embodimentsfor AF, if the personal digital record includes AF regions near the PV,step 745 will report that PVI anatomic ablation is likely to work. Step745 may also indicate likely successful anatomical regions of interestsuch as right atrial cavotricuspid isthmus ablation for typical atrialflutter, left atrial roof line ablation for left atrial roof-dependentatrial flutter, the posterior left atrial wall or left atrial mitralline.

If other regions of interest away from traditional anatomical targetsare indicated, then steps 750-770 guide and enable therapy at thosesites.

Step 750 considers arrhythmia critical regions of interest in turn. Theanalysis of electrical signals may identify areas of repetitiveactivity, regions of high rate or dominant frequency, drivers withrotational or focal activity, regions of low voltage suggesting scar,signal signatures (FIGS. 5B, 5C) or other regions of interest. The sizeof these regions is also identified from intracardiac data or fromnon-invasive data to tailor the size of the mapping tool and therapytool appropriately. In some cases, individual operators may have apreferred definition of critical region. The device can accommodate aplurality of these critical regions, and thus be used by multipleoperators in different patient types. Different critical regiondefinitions may on occasion coincide in any given patient. For instance,in AF, sites of scar may be adjacent to sites of potential drivers.Several other potential coincident sites may occur and can be providedto the physician operator for him/her to make a decision on which totarget.

In some embodiments, regions are identified from a small mappingcatheter 115 inside the heart that provides high resolution recordings.The signals from the sensing catheter are analyzed to determine adirection in which to move towards a region of interest (e.g., towards asource or other target region). In a related embodiment, thisdirectionality is augmented by recording data 552 from non-invasivedevices 110. In each case, the device provides a path in which to movethe catheter to get closer to the target. If the non-invasive recordingssuggest sites in the left atrial roof, then an invasive catheter couldbe moved in that direction.

Step 760 determines if the AF mapping catheter is overlying a criticalregion of interest. The catheter size is important to assess this and isselected using the personal digital record to tailored to the expectedsize(s) for the patient. If the mapping catheter does not overlie thecritical region, the computing server 140 continues to guide navigation.This again can be guided by invasive 115 or non-invasive 110identification of sites of interest.

In step 765, if the mapping device 115, 554 or another tool overlays acritical region, this region is now targeted for therapy. In someembodiments, a catheter inside the heart that performs mapping may alsoprovide ablation energy to do this in a single (one) shot. In otherembodiments, a separate energy delivery (ablation) tool is deployed.

Step 770 assesses the response to therapy, particularly if the region ofinterest has been eliminated. If not, therapy is repeated.

The process then repeats steps from 750, navigating to and ablatingregions of interest until they are all eliminated. The total number ofregions treated is determined in real-time by the electrical signalsavailable (steps 700, 730) and the expected numbers from theclassification for patients with a similar profile (personal digitalrecord).

In another embodiment, all regions of interest are identifiedsimultaneously using a global mapping from another catheter inside theheart such as a large multipole spherical catheter (basket), ornon-invasive methods as discussed. In another embodiment, navigation isapplied only to the treatment tool rather than to the wide-area mappingcatheter.

FIG. 8 is a conceptual diagram illustrating personalized guidance ofablation therapy, according to some embodiments. Step 880 illustrates asensing tool (e.g., a mapping catheter) some distance from a region ofinterest. The system analyzes electrical waves to determine if themapping catheter device overlays a region of interest, for example,signals representing the region of interest overlay as many electrodesof the mapping tool as possible. In some embodiments, the area of thesensor that covers the region of interest is maximized There are manypotential regions of interest, such as those in paragraph 165 above. Ifthe operator is examining repetitive activity as a critical region,repetitive activity in the center of the mapping field indicates thatthe device is centrally placed over this region. If the repetitiveactivity is at one edge of the mapping device, then energy may bedelivered, but the device should then be moved in the direction of thatedge to attempt to maximize the number of sensing elements of the devicethat overlay the repetitive activity. If the operator is targeting highrate or dominant frequency, the same logic is applied. Similarly, if theoperator is targeting regions of low voltage indicative of scar, orregions exhibiting signatures identified by the device. If the operatoris targeting a focal source for an atrial tachycardia or ventriculartachycardia, vectorial analysis is used to indicate the direction of thesource. If the operator is targeting drivers for a complex rhythmdisorder such as atrial fibrillation, which may be focal or rotational,then modified vectorial analysis will indicate the direction of source.The modification for atrial or ventricular fibrillation is that activityexiting an AF source varies from beat to beat (cycle to cycle), and sothe vectorial analysis has to take an average over multiple cycles toidentify the predominant vectorial direction for analysis.

Step 880 illustrates an example that the mapping catheter does notoverlay the region of interest. The system then provides navigationinformation to direct the catheter towards the closest region ofinterest. This is displayed on a portable display such as a dedicatedportable device or a smartphone app, or on a dedicated medical displayunit. Each of these units has appropriate data security and privacysafeguards in place. This navigation step is iterated 885. In step 895the mapping tool has been determined to overlay the region of interest.This is termed the treatment position. The display tool may indicate“Optimal position, ablate.” Ablation can now be performed in someembodiments with the same mapping/ablation probe. For example, the probeis capable of delivering energy to modify tissue regions related to theheart rhythm disorder. In another embodiment, a separate ablationcatheter can be inserted. The process now repeats again in steps 880onwards until the operator determines that sufficient regions ofinterest have been treated. This may be all regions or a numberdetermined by the personal digital record for patients of this typerelative to the location and size of regions.

Example Algorithm for Direction Guidance

FIG. 9A is a graphical illustration of a flowchart depicting an exampleprocess that is executable by software algorithm for a computing system(e.g., computing server 140) to perform a directional guidance forarrhythmias, in accordance with one or more embodiments. The softwarealgorithm may be stored as computer instructions that are executable byone or more general processors (e.g., CPUs, GPUs). The instructions,when executed by the processors, cause the processors to perform varioussteps described in the process. Body signals 950 may be sensed by a bodysurface device 110. The signals may be raw or processed by one or moredata processing pipeline discussed in FIG. 4. The features 952 of thebody signals 950 are extracted using methods such as spectral orinstantaneous phase analysis in single or combinations of electrodes.Other features may include features based in the temporal domain of thesignal and their first and second derivative, such as percentiles,number of local maxima or minima, and features extracted from theautocorrelation. Other features could be extracted from the parametricor signature analysis referred in FIG. 5B. The feature extraction couldbe carried out without the use of the patient's anatomy extracted frommedical image (CT, MRI) techniques. Features are integrated withclinical variables 954 such as age, gender into a statisticalclassifier. Multiple statistical and machine learning approaches 956 maybe used to integrate these features, including correlation coefficientsfrom multivariate regression or supervised machine learning usingconvolutional neural networks or support vector machines trained to aspecific output label of AF termination, long-term outcome, success rateof specific drug or ablative therapies or other labels, duringalgorithmic development. Step 960 shows that these integrated featuresare input into a personal digital record-based arrhythmia predictions,which can identify the specific phenotype of the patient disease such asa likely PV based AF, or AF from sites that arise away from the PVs, orVT that arises from sites common in patients with that phenotype.

In step 962, the computing server 140 may determine directionality usinga non-invasive device as guidance to guide a probe (e.g., a catheter)towards one of the locations of the heart that are associated with theheart rhythm disorder. Directionality analysis allows to identify thecardiac region from which the electric disturbance is arising andtherefore the target for ablation. The computing server 140 may identifythese cardiac regions with no basic assumption of their sustaining orinitiating mechanism (reentrant activity, focal activity, repetitiveactivity, tachy-pacing, multiple waves), and identify those sites fromwhich the electric activity propagates to the rest of the heart andinitiates or maintains the arrhythmia. In one embodiment, thedirectionality analysis can distinguish activity propagation from leftversus right atria, and provide the direction to these anatomicalchambers. In other embodiment, the directionality analysis can identifythe specific anatomical region maintaining the arrhythmia, such as thepulmonary veins, left or right appendages or other anatomical sites, andprovide the direction to these specific sites.

Step 962 shows that directionality analysis may be used to guide anablation catheter inside the heart, or an external ablation source (suchas proton beam irradiation) to the critical region of interest, e.g., asource or target region of the arrhythmia. The location algorithm mayidentify the position of the ablation catheter relative to the region ofinterest in the heart, and guide the ablation catheter to the region ofinterest. The ablation catheter is then analyzed to obtain a ratio ofthe number of electrodes of the mapping device (e.g., a mappingcatheter) that cover the region of interest at step 964. This is done bydetermining the area of the sensor that covers the predicted region ofinterest, as a ratio of the entire sensed area. Item 966 determineswhether the area ratio exceeds a desired ratio threshold. In someembodiments, if this ratio exceeds a threshold, such as 0.75, (threequarters of the mapping device overlaps the region of interest), thentherapy is applied at this site in step 968. In other embodiments, itmay be permissible to apply therapy if this ratio exceeds 0.5 or someother threshold values. If the ratio is low, in other words the devicehas only a small overlap with a region of interest, then in someembodiments the system provides directionality guidance to move atreatment device to increase overlap with the region of interest beforeapplying therapy (steps 962-968).

For directionality analysis from a device inside the heart, signals atthe sensor site are processed and used to calculate the direction inwhich to move the electrode array to reach or navigate to the source.This is analogous to global positioning systems which use the currentposition to navigate to a desired location, without examining the entiremap of the globe or remote sites. This approach enables higherresolution mapping than currently available in wide-area global orpanoramic mapping systems within the heart.

In some embodiments, directionality analysis can be performed usingcombination of body surface signals and signals at the probe. Forexample, the probe contains sensors for recording and may be referred toas a catheter sensor. FIG. 9B is a graphical illustration of a guidancesystem that integrates data from sensing devices on the body surface(such as FIGS. 3A, 3B) and sensing devices inside the heart (such asstep 554 in FIG. 5B) to direct therapy, executable by softwarealgorithms for a computing system (e.g., computing server 140) inaccordance with one or more embodiments. The software algorithm may bestored as computer instructions that are executable by one or moregeneral processors (e.g., CPUs, GPUs). The instructions, when executedby the processors, cause the processors to perform various stepsdescribed in the process. Signals from these devices may be raw orprocessed by one or more data processing pipeline discussed in FIG. 4.Features of body signals and intracardiac signals can be extracted usingmethods described above such as spectral or phase analysis, or timedomain features of signals from single or combinations of electrodes.

Step 962 takes guidance direction from an internal catheter input (alsoFIG. 9A) and step 970 takes guidance direction from the body surfaceinput. Steps 972 to 994 combine these two input data sets to guide adevice or catheter towards a region of interest for a heart rhythmdisorder in relation to the body surface device. The embodiment focuseson providing directional information in the form of left/right/up/downguidance towards a functional region of interest, agnostic to locationwithin the heart (or other organs). This is quite distinct from the moregeneral prior art applications of ascertaining catheter position withinthe heart (i.e. three dimensional catheter navigation), that is notfocused on a specific function (e.g., heart rhythm disorder). Theembodiment compares functional information in vectors, spatialactivation or timing between the body surface and catheter inputs for aspecific heart rhythm disorder in a specific patient. The embodimentthus uses the body surface patch to provide global information ondirection towards a functional region of interest, avoiding the need fora system that creates an anatomical 3D reconstruction.

Step 972 compares the two body surface and catheter inputs. In someembodiments, inputs are compared by vectors in a vectorial analysis. Inone approach, the embodiment determines similarity of vectors towards asource or region of interest for both inputs within a threshold. Forinstance, a threshold of 45 degrees would indicate a confidence intervalof plus/minus 22.5 degrees about a core vector. Other thresholds can beapplied, depending on the quality of signals, the rhythm underconsideration and location within the heart. Vectors can be comparedusing multiple mathematical approaches including correlationcoefficients from multivariate regression, or supervised machinelearning using convolutional neural networks or support vector machines.Machine learning can be trained to specific output labels of vectorialdirection to regions where therapy was acutely successful (one outcomelabel), produced good long-term freedom from arrhythmia after therapy (asecond outcome label) or produced good quality of life after therapybased on clinical determination (a third outcome label). Trainingtechniques of machine learning models are further discussed in FIG. 6.

In other embodiments, steps 972 to 994 compare other (non-vectorial)data between inputs to calculate directions in which to move a catheter.Some embodiments compare spatial differences in patterns of electricalactivation over time. For instance, if the body surface indicates afocal beat with activation that emanates radially outward, the cathetercan be directed until its pattern of activation matches this focal beatpattern. If the body surface input indicates a rotational activationpattern with a certain time periodicity, the catheter can be moved untilit mimics this rotational pattern. Other spatial patterns between inputswill be evident to those familiar with the art. Of note, there issmoothing of activation and other differences between the body surfaceand catheter in the heart, and confidence intervals must be includedinto comparisons between these inputs. Some activation patterns incomplex rhythms such as atrial fibrillation are more difficult toquantify as intuitive spatial patterns, but can be compared in terms ofsimilar frequency rate, similar organizational index (width of spectraldominant frequency), similar disorganization (from metrics such asShannon entropy), and other parameters that will be familiar to apractitioner familiar with the art.

In yet other embodiments, steps 972 to 994 compare temporal (timing)data in electrical information between inputs to calculate directions inwhich to move a catheter. For instance, in the complex arrhythmia ofatrial fibrillation, if activation times in a localized region of thebody surface input span the cycle length (typically 150-220 ms), thenthe catheter will be moved until its recordings also span this cyclelength. In an atypical atrial flutter or ventricular tachycardia,conduction may be slow with a prolonged activation time sequence througha reentrant isthmus or near a scar borderzone. The steps 972 to 994 willguide the catheter until its activation time sequence matches that onthe body surface. Other less intuitive timing metrics include spectraltiming, spectral organization and phase, each of which can be comparedbetween inputs to provide guidance information to the catheter.

In some embodiments, the spatial, vectorial, and timing comparisons canbe combined or blended to provide catheter guidance, depending on thespecific case, specific heart rhythm disorder, patient characteristics,database of stored patterns and operator preference. Specific steps toenable each of these functions are now outlined. All steps described areillustrative and not designed to be an exhaustive list of permutationsof these inventive elements.

In step 974, signal quality of both the catheter and body surfacesignals are analyzed to create a confidence level in each. Quality ofthe catheter and body surface signals and/or directions can beidentified using signal-to-noise ratio algorithms, extraction ofnoise-related features as described in 952, by using specific machinelearning algorithms trained with noisy directions and signals, and othertechniques that will be evident to a practitioner with ordinary skill inthe art. In step 974, if both body surface and catheter directionalsignals have high quality, compared with a specific quality threshold,then their relative vectors are determined. In step 976, if thesevectors are similar, within a threshold as discussed, then thatdirectional vector is used to provide guidance to the user.

If vectors are not similar, one of the vectors may be prioritized 978based on past records and data from a database of procedures (e.g.,predetermined rules) \. The prioritized vector serves as the controllingvector for directionality. For example, the prioritization of one vectorover another may be based on various factors such as the location in theorgan (e.g., heart), the rhythm under consideration and patient specificfactors such as age, gender, heart size, and prior surgery orinstrumentation in the heart. In some embodiments for treating atrialfibrillation, if the catheter currently lies near the inter-atrialseptum, the vector from the catheter is prioritized higher because bodysurface signals poorly represent the intra-atrial septum. Conversely, ina patient in whom multiple prior ablations have been performed, bodysurface signals may be prioritized since internal signals may have lowerquality and lower confidence. In general, body surface signals enjoyspatial and temporal filtering due to conduction through body tissues,and thus may show greater temporo-spatial stability of vectors.Accordingly, body surface vectors may be prioritized, when available,for providing a global directional vector.

In another embodiment, steps 976 and 978 move the catheter to reconcilehigh quality data from the body surface input with high quality datafrom the catheter input. As described above, this may involvenon-vectorial information such as spatial or timing information.

Step 980 determines, when signal types are not both of high quality,whether the body surface signal is of high quality (confidence). If so,this signal type is used to determine directional vector. Oftentimesbody surface signals enjoy spatial and temporal filtering due toconduction through body tissues, and thus may show greatertemporo-spatial stability of vectors. Accordingly, for providing aglobal directional vector, body surface signals are prioritized in step982.

In another embodiment, step 982 moves the catheter to reconcile highquality data from the body surface input with data from the catheterinput. As described above, this may involve non-vectorial informationsuch as spatial or timing information. The step 982 will prioritize highquality body surface signals.

If body surface signals are not of high quality, step 984 assesseswhether the internal catheter device signals are of high quality. If so,these signals are used to provide a navigation direction.

In another embodiment, steps 984 to 985 will provide guidanceinformation to move the catheter to reconcile data from the body surfaceinput with high quality data from the catheter input. As describedabove, this may involve non-vectorial information such as spatial ortiming information. The step 982 will prioritize high quality cathetersignals.

In step 986, if neither the body surface nor catheter signals are ofsufficient quality, past records and data from a database of procedures(e.g., predetermined rules) may be used to provide a probabilisticdirectionality. Several clinical and mapping features may be used. Insome embodiments, such as for atrial fibrillation, clinical guidance maysuggest directions towards the right atrium in patients with multipleprior ablations in the left atrium. Conversely, in a patient with noprior ablation who is relatively young and with few other medicalproblems, guidance may direct the treatment device towards the pulmonaryveins. Step 986 may integrate these features to create a personaldigital record-based arrhythmia prediction. This also representsphenotypes such as patients with atrial fibrillation near the pulmonaryveins, or atrial fibrillation from other sites (particularly in patientswith prior diseases or in whom pulmonary vein ablation has not worked),or ventricular tachycardia that arises from common sites such as nearventricular scar, near the ventricular outflow tracts or other sitesmore common in patients with different features. The database forproviding directionality guidance is constructed based on detailedmapping in patients of many different types, and includes response toablation of regions of interest.

In another embodiment, step 986 will provide guidance information tomove the catheter to reconcile data from the body surface input withdata from the catheter input. As described above, this may involvenon-vectorial information such as spatial or timing information. If bothdata inputs are of lower quality, step 986 will use a database relatingstored patterns or timing of successful and unsuccessful sites which canbe matched to the characteristics of the current patient.

Directional guidance in some embodiments can be implemented by deeplearning classifiers trained with previous and stored clinical data.Deep learning models may comprise neural networks, traditional machinelearning model, or statistical models. In one example embodiment, themachine learning model is trained to identify the direction (vector)from the catheter to region of interest, the electrode or subset ofelectrodes of the catheter closer to region of interest, or other. Theoutput of this machine learning model can be used to guide the catheter962 to the region of interest. Training techniques of machine learningmodels are further discussed in FIG. 6.

FIG. 9C is a graphical illustration of a flowchart depicting an exampleprocess that is executable by software algorithm for a computing system(e.g., computing server 140) to use directional guidance from a catheterinside the heart to guide an ablation catheter inside the heart. Item900 shows sensing devices of many forms. The multipole device 904 showsa high resolution multipolar spade catheter, basket device 908 shows amultipolar basket catheter, and multiple other types exist 912.

The flowchart starts at timesteps 920 starting at t. Neighboringelectrodes are identified at step 924 as physically adjacent, with knownelectrode spacings. Step 928 computes direction of electricalpropagation using electrode signals integrated over the timestep t,shown previously in FIG. 8. First, the system spatially interpolates thewavefront √ by electrodes at known spacing on the array. For each pointi along this interpolated wavefront √ at time t, the system searcheswithin a circle for the point j at the next time step with the mostsimilar gradient. The system infers that the activation wavefront hastraveled from point i to point j in this time, and marks this flow withan instantaneous flow vector (propagation over time). Step 932 repeatsthe computation of flow (directionality) across regions of the electrodearray. Step 936 repeats this process for subsequent timesteps.

Step 945 illustrates multiple electrograms over windows of 150 ms, withdotted lines indicating flow computed from electrograms as indicated.Directionality is now integrated over the entire available number ofelectrodes on the array to determine the average direction of electricalflow. Directionality could be extracted from feature analysis asexplained in FIG. 5A or using specific signatures identified as in FIG.5B. The average direction of electrical flow is capable of describingcomplex spatiotemporally changing fibrillation 945. Guiding the sensorin the reverse direction will thus move closer to the nearest source orother target region. This approach improves upon the accuracy providedby a single electrode which has historically not been able to findcritical regions of interest for many heart rhythm disorders such asfibrillation.

For AF, candidate ablation targets include mathematical combinations ofelectrogram features plus comorbidities (e.g., body mass index,diabetes, hypertension), demographics (e.g., age, gender, prior ablationor not) and, if available, genetic, metabolic and biomarker information.Novel electrogram targets can extend ‘traditional’ targets to targetssuch as repetitive patterns, or transient rotations or focal patterns,or interrupted rotational or focal patterns, may be critical tomaintaining arrhythmia in some individuals, as previously discussed.This embodiment defines these electrogram features, by determining inindividual patients which may be related to favorable outcomes. Thisthen becomes a numerical classification within the digitalclassification (i.e. classification) as data from more individuals islabeled and accumulated.

Depending on the patient, therapy targets may be rotational or focalsources/drivers, or other electrical features—regardless of structure.Intermediate phenotypes may be present in phenotypes in specificindividuals (electrical and structural, which may dynamically changewith e.g., changes in health status). Again, multiple forms of theelectrical pattern may colocalize with such structural elements. Thecomputing server 140 may store electrical signals associated with thesesites to update the personal and population databases. Therapy mayinclude the destruction of tissue by surgical or minimally invasiveablation, to modulate via electrical pacing or mechanical pacing, orusing gene, stem cell, or drug therapy. Medications may include class Iagents to decrease atrial conduction velocity, or class III agents toprolong refractoriness. AF ablation may not just eliminate tissue, buttarget areas bordering fibrosis or areas of electrical vulnerability.Therapy can also be directed to related tissue to these regions, theirnerve supply, or other modulating biological systems.

FIG. 10A is a graphical illustration of an example of a patient withinitiating beats for AF near PVs. Signals from a single surfaceelectrocardiographic lead showing the segment under analysis 1000, whosedirectionality maps were obtained at 3 temporal points from the AFinitiating interval 1005. Directional analysis of time instants 1010 and1015 on the right and left atria showed centrifugal activation from theposterior left atrial wall, whereas instant 1020 showed a centrifugalactivation from the right pulmonary veins and a secondary centrifugalactivation from the right atrial appendage. In this case, subsequentablation near the pulmonary veins (pulmonary vein isolation) waseffective at eliminating the AF in the long term.

FIG. 10B is a graphical illustration of an example of a patient withinitiating beats for AF from PV and non-PV sites. Surfaceelectrocardiographic signal showing the segment under analysis 1025,whose directionality maps were obtained at 4 temporal points from the AFinitiating intervals 1030 and 1035. Directional analysis of time instant1040 on the right and left atria showed a centrifugal activation fromthe left pulmonary veins and from the right atrial appendage.Directional analysis of time instant 1045 showed a centrifugalactivation from the right atrial appendage. Directional analysis of timeinstant 1050 showed a centrifugal activation from the posterior leftatrial wall, and directional analysis of instant 1055 showed again acentrifugal activation from the right atrial appendage. In this case,subsequent ablation near the pulmonary veins (pulmonary vein isolation)reduced AF on follow-up but did not eliminate it.

FIG. 10C is a graphical illustration of an example of a patient withinitiating beats for AF remote from PVs. Surface electrocardiographicsignal showing the segment under analysis 1060, whose directionalitymaps were obtained at 3 temporal points from the AF initiating intervals1065 and 1070. Directional analysis of time instant 1075 on the rightand left atria showed a centrifugal activation from the right atrialappendage. Directional analysis of time instant 1080 showed acentrifugal activation from the right pulmonary veins, and directionalanalysis of time instant 1085 showed a centrifugal activation from theinferior cava vein. In this case, subsequent ablation near the pulmonaryveins (pulmonary vein isolation) did not eliminate AF in the long termand a repeat ablation procedure was required.

FIG. 10D is a graphical illustration of activation in the atrial in apatient with sustaining regions for AF near PVs. Non-invasivereconstruction of the reentrant activity during atrial fibrillationshowed primary reentrant sources near the left and right pulmonary veinsand absence of reentrant activity elsewhere. In this patient, AF acutelyterminated after pulmonary vein isolation by radiofrequency ablation.This indicates that driving regions for AF can be identified from thedevice non-invasively, and used in this case to predict that PVIablation will be effective in this patient.

FIG. 10E is a graphical illustration of an example of a patient withsustaining regions for AF in the right atrium, remote from PVs.Non-invasive reconstruction of the reentrant activity during atrialfibrillation showed primary reentrant sources in the right atrium andabsence of reentrant sources near the pulmonary veins. This patient didnot acutely terminate AF after pulmonary vein isolation. This indicatesthat driving regions for AF outside the PV regions can be identifiedfrom the device non-invasively, and used in this case to predict thatPVI ablation is less likely to be effective alone at preventinglong-term recurrence in this patient.

FIGS. 10A-E indicate regions of triggers or sources which areillustrated as a heat map on the representation of the heart that may bedisplayed in a graphical user interface. If multiple triggers or sourcesare identified across multiple beats or initiations of the rhythm, eachof these triggers can be aggregated or integrated into this said heatmap. The heat map can be a simple accumulation of the information foreach of the regions of interest. It may also be an arithmetic mean or ageometric mean designed to emphasize the region of interest overbackground activity.

A heat map for a heart rhythm disorder in a subject may be generatedbased on one or more directionality maps such as by aggregating thedirectionality maps. A directionality map may be generated for the heartrhythm disorder based on electrical signals measured by a body surfacedevice 110. The directionality map may describe pathways that indicatebeats that initiate an onset of the heart rhythm disorder. Thedirectionality map may be generated by applying a trained machinelearning model to the electrical signals, wherein the machine learningmodel is trained on training examples comprising electrical signals ofhuman hearts and known source regions of the heart rhythm disorder.Source regions for the heart rhythm disorder may be determined. In turn,a heat map may be generated based on the determined information anddirectionality maps.

Computing Machine Architecture

FIG. 11 is a block diagram illustrating components of an examplecomputing machine that is capable of reading instructions from acomputer-readable medium and execute them in a processor (orcontroller). A computer described herein may include a single computingmachine shown in FIG. 11, a virtual machine, a distributed computingsystem that includes multiples nodes of computing machines shown in FIG.11, or any other suitable arrangement of computing devices.

By way of example, FIG. 11 shows a diagrammatic representation of acomputing machine in the example form of a computer system 1100 withinwhich instructions 1124 (e.g., software, source code, program code,expanded code, object code, assembly code, or machine code), which maybe stored in a computer-readable medium for causing the machine toperform any one or more of the processes discussed herein may beexecuted. In some embodiments, the computing machine operates as astandalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment.

The structure of a computing machine described in FIG. 11 may correspondto any software, hardware, or combined components shown in FIG. 1A,including but not limited to, the client device 120, the physiciandevice 132, the computing server 140, and various engines, interfaces,terminals, and machines in this disclosure. While FIG. 11 shows varioushardware and software elements, each of the components described in FIG.1A may include additional or fewer elements.

By way of example, a computing machine may be a personal computer (PC),a tablet PC, a set-top box (STB), a personal digital assistant (PDA), acellular telephone, a smartphone, a web appliance, a network router, aninternet of things (IoT) device, a switch or bridge, or any machinecapable of executing instructions 1124 that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” and “computer” may also be taken to include anycollection of machines that individually or jointly execute instructions1124 to perform any one or more of the methodologies discussed herein.

The example computer system 1100 includes one or more processors 1102such as a CPU (central processing unit), a GPU (graphics processingunit), a TPU (tensor processing unit), a DSP (digital signal processor),a system on a chip (SOC), a controller, a state equipment, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or any combination of these. Parts of the computingsystem 1100 may also include a memory 1104 that store computer codeincluding instructions 1124 that may cause the processors 1102 toperform certain actions when the instructions are executed, directly orindirectly by the processors 1102. Instructions can be any directions,commands, or orders that may be stored in different forms, such asequipment-readable instructions, programming instructions includingsource code, and other communication signals and orders. Instructionsmay be used in a general sense and are not limited to machine-readablecodes. One or more steps in various processes described may be performedby passing through instructions to one or more multiply-accumulate (MAC)units of the processors.

One and more methods described herein improve the operation speed of theprocessors 1102 and reduces the space required for the memory 1104. Forexample, the signal processing techniques and machine learning methodsdescribed herein reduce the complexity of the computation of theprocessors 1102 by applying one or more novel techniques that simplifythe steps in training, reaching convergence, and generating results ofthe processors 1102. The algorithms described herein also reduces thesize of the models and datasets to reduce the storage space requirementfor memory 1104.

The performance of certain of the operations may be distributed amongthe more than processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations. Even though in thespecification or the claims may refer some processes to be performed bya processor, this should be construed to include a joint operation ofmultiple distributed processors.

The computer system 1100 may include a main memory 1104, and a staticmemory 1106, which are configured to communicate with each other via abus 1108. The computer system 1100 may further include a graphicsdisplay unit 1110 (e.g., a plasma display panel (personal digitalrecord), a liquid crystal display (LCD), a projector, or a cathode raytube (CRT)). The graphics display unit 1110, controlled by theprocessors 1102, displays a graphical user interface (GUI) to displayone or more results and data generated by the processes describedherein. The computer system 1100 may also include alphanumeric inputdevice 1112 (e.g., a keyboard), a cursor control device 1114 (e.g., amouse, a trackball, a joystick, a motion sensor, or other pointinginstrument), a storage unit 1116 (a hard drive, a solid state drive, ahybrid drive, a memory disk, etc.), a signal generation device 1118(e.g., a speaker), and a network interface device 1120, which also areconfigured to communicate via the bus 1108.

The storage unit 1116 includes a computer-readable medium 1122 on whichis stored instructions 1124 embodying any one or more of themethodologies or functions described herein. The instructions 1124 mayalso reside, completely or at least partially, within the main memory1104 or within the processor 1102 (e.g., within a processor's cachememory) during execution thereof by the computer system 1100, the mainmemory 1104 and the processor 1102 also constituting computer-readablemedia. The instructions 1124 may be transmitted or received over anetwork 1126 via the network interface device 1120.

While computer-readable medium 1122 is shown in an example embodiment tobe a single medium, the term “computer-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 1124). The computer-readable medium mayinclude any medium that is capable of storing instructions (e.g.,instructions 1124) for execution by the processors (e.g., processors1102) and that cause the processors to perform any one or more of themethodologies disclosed herein. The computer-readable medium mayinclude, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media. Thecomputer-readable medium does not include a transitory medium such as apropagating signal or a carrier wave.

EXAMPLE EMBODIMENTS

All features of various embodiments described in this section can becombined with another embodiment described in this section or anyembodiments described in other figures.

In some embodiments, the techniques described herein relate to a systemincluding: a body surface device carrying a plurality of electrodesconfigured to be in contact with a body surface of a subject, theelectrodes configured to cover one or more spatial projections of one ormore areas of a heart projected on the body surface, wherein theelectrodes are configured to detect a plurality of electrical signalsgenerated by the heart of the subject, wherein the body surface deviceis configured to record from an area of less than one half of torsosurface of the subject; and a computing device configured to receivesignal data generated from the body surface device, the computing deviceincluding a processor and memory, the memory, when executed by theprocessor, causes the processor to perform operations including:determining one or more locations of the heart that are associated witha heart rhythm disorder based on the signal data.

In some embodiments, the techniques described herein relate to a system,wherein the operations performed by the processor further include:computing a predicted success score for a planned therapy foreliminating one or more regions that initiate an onset of the heartrhythm disorder or regions that maintain the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a system,wherein the planned therapy targets pulmonary veins.

In some embodiments, the techniques described herein relate to a system,wherein the planned therapy targets regions are in the left side orright side of the heart.

In some embodiments, the techniques described herein relate to a system,wherein the heart rhythm disorder is atrial fibrillation.

In some embodiments, the techniques described herein relate to a system,wherein the operations performed by the processor further include:guiding a probe towards one of the locations of the heart that areassociated with the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a system,wherein the probe contains sensors for recording.

In some embodiments, the techniques described herein relate to a system,wherein the probe is capable of delivering energy to modify tissueregions related to the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a system,wherein the computing device is a computing server that isgeographically remote from the body surface device.

In some embodiments, the techniques described herein relate to a system,wherein the body surface device further includes a substrate thatincludes one or more regions, each region configured to be in contactwith one of torso quadrants of the subject, the torso quadrants being aright anterior, a left anterior, a left posterior, and a rightposterior, wherein the substrate includes at least one region configuredto be in contact with at least one of the torso quadrants.

In some embodiments, the techniques described herein relate to a system,wherein determining the one or more locations of the heart that areassociated with the heart rhythm disorder includes a phase analysis, ananalysis of spatial patterns of electrical activation over time, avectorial analysis, a spectral analysis, and/or signal featurization.

In some embodiments, the techniques described herein relate to a system,wherein determining the one or more locations of the heart that areassociated with the heart rhythm disorder includes determining whetherone of the locations is the left atrium, the right atrium, the leftventricle, or the right ventricle of the heart of the subject.

In some embodiments, the techniques described herein relate to a system,wherein determining one or more locations of the heart that areassociated with the heart rhythm disorder includes inputting a versionof the signal data to one or more machine learning models to determineone of the locations, at least one of the machine learning models areiteratively trained based on training samples of data associated withknown heart rhythm disorders.

In some embodiments, the techniques described herein relate to a system,wherein the operations performed by the processor further include:calculating a cardiac output; determining if the cardiac output isreduced; and sending an alert that the cardiac output is reduced.

In some embodiments, the techniques described herein relate to a system,wherein the electrodes are configured to cover a spatial projection ofat least a majority of a heart chamber projected on the body surface.

In some embodiments, the techniques described herein relate to a system,wherein the one or more locations of the heart that are associated withthe heart rhythm disorder include: a location of beat that initiatesonset of a heart rhythm disorder, and/or a location of a source regionof the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a system,wherein the body surface device is wearable during daily activities ofthe subject.

In some embodiments, the techniques described herein relate to a bodysurface device wearable by a subject, the body surface device including:a plurality of electrodes configured to be in contact with a bodysurface of the subject, the electrodes configured to cover one or morespatial projections of one or more areas of a heart projected on thebody surface, wherein the electrodes are configured to detect aplurality of electrical signals generated by the heart of the subject,wherein the body surface device is configured to record from an area ofless than one half of torso surface of the subject; and a transmitterconfigured to transmit a version of signal data for the plurality ofelectrical signals for a computing device that is configured todetermine one or more locations of the heart that are associated with aheart rhythm disorder based on the signal data.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the plurality of electrodes are configured todetect the electrical signals respectively from the left atrium, theright atrium, the left ventricle, or the right ventricle of the heart.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein determining one or more locations of the heartthat are associated with the heart rhythm disorder includes determiningwhether one of the locations is the left atrium, the right atrium, theleft ventricle, or the right ventricle of the heart of the subject.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the one or more locations of the heart that areassociated with the heart rhythm disorder include: a location of beatthat initiates onset of a heart rhythm disorder, and/or a location of asource region of the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is further configured tocomputer a predicted success score for a planned therapy for eliminatingone or more regions that initiate an onset of the heart rhythm disorderor regions that maintain the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is a computing server thatis geographically remote from the body surface device.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is an electronic deviceused by the subject.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the electrodes are configured to cover a spatialprojection of at least a majority of a heart chamber projected on thebody surface.

In some embodiments, the techniques described herein relate to a bodysurface device, further including: a substrate including one or moreregions, each region configured to be in contact with one of torsoquadrants of the subject, the torso quadrants being a right anterior, aleft anterior, a left posterior, and a right posterior, wherein thesubstrate includes at least one region configured to be in contact withat least one of the torso quadrants.

In some embodiments, the techniques described herein relate to a methodincluding: receiving signal data generated from a body surface device, abody surface device carrying a plurality of electrodes configured to bein contact with a body surface of a subject, the electrodes configuredto cover one or more spatial projections of one or more areas of a heartprojected on the body surface, wherein the electrodes are configured todetect a plurality of electrical signals generated by the heart of thesubject, wherein the body surface device records from an area of lessthan one half of torso surface of the subject; and determining one ormore locations of the heart that are associated with a heart rhythmdisorder based on the signal data.

In some embodiments, the techniques described herein relate to a method,wherein the one or more locations of the heart that are associated withthe heart rhythm disorder include: a location of beat that initiatesonset of a heart rhythm disorder, and/or a location of a source regionof the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a method,wherein determining one or more locations of the heart that areassociated with the heart rhythm disorder includes a phase analysis, ananalysis of spatial patterns of electrical activation over time, avectorial analysis, a spectral analysis, and/or signal featurization.

In some embodiments, the techniques described herein relate to a method,further including: computing a predicted success score for a plannedtherapy for eliminating one or more regions that initiate an onset ofthe heart rhythm disorder or regions that maintain the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a method,further including: guiding a probe towards one of the locations of theheart that are associated with the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a method,wherein determining one or more locations of the heart is based onanalysis of the electrical signals that identifies one or more of thefollowing: areas of repetitive activity, regions of high rate ordominant frequency, drivers with rotational or focal activity, regionsof low voltage suggesting scar, and/or signal signatures.

In some embodiments, the techniques described herein relate to a method,further including: generating a directionality map for the heart rhythmdisorder based on the electrical signals, the directionality mapdescribing pathways that indicate beats that initiate an onset of theheart rhythm disorder; determining source regions for the heart rhythmdisorder, and generating a heat map for the heart rhythm disorder in thesubject based on the directionality map.

In some embodiments, the techniques described herein relate to a method,wherein generating the directionality map includes applying a trainedmachine learning model to the electrical signals, wherein the machinelearning model is trained on training examples including electricalsignals of human hearts and known source regions of the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a methodfor treating a heart rhythm disorder, the method including: receivingsignal data for electrical signals of a heart detected by a plurality ofsensing electrodes carried on a body surface device worn by a subject,the electrodes covering one or more spatial projections of one or moreareas of a heart projected on a body surface of the subject; generatinga directionality map for a probe based on the electrical signals toidentify tissue for one of: a location of beat that initiates onset of aheart rhythm disorder in the directionality map, or a location of asource region of the heart rhythm disorder in the directionality map;and providing directional information from the directionality map toguide the probe towards a region of interest to treat the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a method,further including determining one or more locations of the heart thatare associated with the heart rhythm disorder based on a phase analysis,an analysis of spatial patterns of electrical activation over time, avectorial analysis, a spectral analysis, and/or signal featurization.

In some embodiments, the techniques described herein relate to a method,wherein generating the directionality map includes applying a trainedmachine learning model to the electrical signals, wherein the machinelearning model is trained on training examples including electricalsignals of human hearts and known source regions of the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a method,further including: computing a predicted success score for a plannedtherapy for eliminating one or more regions that initiate an onset ofthe heart rhythm disorder or regions that maintain the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a method,further including: identifying the region of interest by the signal datadetected by the body surface device; determining a number of a secondset of electrodes carried by the probe that overlap with the region ofinterest to determine an area overlap; and causing, responsive to thearea overlap being higher than a threshold, the probe to modify a tissueregion at the region of interest.

In some embodiments, the techniques described herein relate to a method,wherein the directional information is generated further based on pastrecords of the subject and data from a database of procedures.

In some embodiments, the techniques described herein relate to a method,wherein identifying the tissue is based on analysis of the electricalsignals that identifies one or more of the following: areas ofrepetitive activity, regions of high rate or dominant frequency, driverswith rotational or focal activity, regions of low voltage suggestingscar, and/or signal signatures.

In some embodiments, the techniques described herein relate to a method,wherein the probe contains sensors for generating a second set of signaldata for electrical signals of the heart detected by the sensors.

In some embodiments, the techniques described herein relate to a method,further including: generating a first directional vector from the signaldata detected by the body surface device; generating a seconddirectional vector from the second set of signal data detected bysensors of the probe; and generating a final directional vector thatguides the probe based on the first directional vector and the seconddirectional vector.

In some embodiments, the techniques described herein relate to a method,wherein the body surface device records from an area of less than onehalf of torso surface of the subject.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium for storing computer codeincluding instructions, the instructions, when executed by one or moreprocessors, cause the one or more processors to perform operations fortreating a heart rhythm disorder, the operations including: receivingsignal data for electrical signals of a heart detected by a plurality ofsensing electrodes carried on a body surface device worn by a subject,the electrodes covering one or more spatial projections of one or moreareas of a heart projected on a body surface of the subject; generatinga directionality map for a probe based on the electrical signals toidentify tissue for one of: a location of beat that initiates onset of aheart rhythm disorder in the directionality map, or a location of asource region of the heart rhythm disorder in the directionality map;and providing directional information from the directionality map toguide the probe towards the identified tissue to treat the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the operations furtherinclude: generating a directionality map describing pathways of heartrhythms based on the electrical signals.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein generating thedirectionality map includes applying a trained machine learning model tothe electrical signals, wherein the machine learning model is trained ontraining examples including electrical signals of human hearts and knownsource regions of the heart rhythm disorder.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the operations furtherinclude: generating a directionality map for the heart rhythm disorderbased on the electrical signals, the directionality map describingpathways that indicate beats that initiate an onset of the heart rhythmdisorder; determining source regions for the heart rhythm disorder, andgenerating a heat map for the heart rhythm disorder in the subject basedon the directionality map.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the operations furtherinclude: identifying the region of interest by the signal data detectedby the body surface device; determining a number of a second set ofelectrodes carried by the probe that overlap with the region of interestto determine an area overlap; and causing, responsive to the areaoverlap being higher than a threshold, the probe to modify a tissueregion at the region of interest.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the directioninformation is generated further based on past records of the subjectand data from a database of procedures.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein identifying the tissueis based on analysis of the electrical signals that identifies one ormore of the following: areas of repetitive activity, regions of highrate or dominant frequency, drivers with rotational or focal activity,regions of low voltage suggesting scar, and/or signal signatures.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the probe containssensors for generating a second set of signal data for electricalsignals of the heart detected by the sensors.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the operations furtherinclude: generating a first directional vector from the signal datadetected by the body surface device; generating a second directionalvector from the second set of signal data detected by sensors of theprobe; and generating a final directional vector that guides the probebased on the first directional vector and the second directional vector.

In some embodiments, the techniques described herein relate to anon-transitory computer-readable medium, wherein the body surface devicerecords from an area of less than one half of torso surface of thesubject.

In some embodiments, the techniques described herein relate to a bodysurface device wearable by a subject, the body surface device including:a substrate including one or more regions, each region configured to bein contact with one of torso quadrants of the subject, the torsoquadrants being a right anterior, a left anterior, a left posterior, anda right posterior, wherein the substrate includes at least one regionconfigured to be in contact with at least one of the torso quadrants;one or more sets of electrodes, each set of electrodes carried in one ofthe regions of the substrate, the one or more sets of electrodesconfigured to detect a plurality of electrical signals generated by aheart of the subject, wherein the set of electrodes, which are carriedin the region configured to be in contact with the right anterior, theleft anterior, the left posterior, or the right posterior, areconfigured to detect the electrical signals for detecting a heart rhythmdisorder respectively from the left atrium, the right atrium, the leftventricle, or the right ventricle; and a transmitter configured totransmit a version of signal data for the plurality of electricalsignals for a computing device that is configured to determine one ormore locations of the heart that are associated with a heart rhythmdisorder based on the signal data.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is a computing server thatis geographically remote from the body surface device.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is an electronic deviceused by the subject.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the electrodes are configured to cover a spatialprojection of at least a majority of a heart chamber projected on thebody surface.

In some embodiments, the techniques described herein relate to a bodysurface device, wherein the computing device is further configured tocomputer a predicted success score for a planned therapy for eliminatingone or more regions that initiate an onset of the heart rhythm disorderor regions that maintain the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a methodfor determining one or more locations associated with a heart rhythmdisorder, the method including: receiving signal data for electricalsignals of a heart detected by a plurality of sensing electrodes carriedon a body surface device worn by a subject, the electrodes covering oneor more spatial projections of one or more areas of a heart projected ona body surface of the subject; inputting a version of the signal data toone or more machine learning models to determine one or more locationsof the heart that are associated with a heart rhythm disorder, at leastone of the machine learning models are iteratively trained based ontraining samples of data associated with known heart rhythm disorders;and determining, using the one or more machine learning models, whetherone of the locations of the heart that are associated with the heartrhythm disorder is the left atrium, the right atrium, the leftventricle, or the right ventricle of the heart of the subject.

In some embodiments, the techniques described herein relate to a method,wherein determining one or more locations of the heart that areassociated with the heart rhythm disorder includes a phase analysis, ananalysis of spatial patterns of electrical activation over time, avectorial analysis, a spectral analysis, and/or signal featurization.

In some embodiments, the techniques described herein relate to a method,further including: computing a predicted success score for a plannedtherapy for eliminating one or more regions that initiate an onset ofthe heart rhythm disorder or regions that maintain the heart rhythmdisorder.

In some embodiments, the techniques described herein relate to a method,further including: guiding a probe towards one of the locations of theheart that are associated with the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a method,further including: identifying the region of interest by the signal datadetected by the body surface device; determining a number of a secondset of electrodes carried by the probe that overlap with the region ofinterest to determine an area overlap; and causing, responsive to thearea overlap being higher than a threshold, the probe to modify a tissueregion at the region of interest.

In some embodiments, the techniques described herein relate to a method,further including generating a directionality map, generating thedirectionality map including applying a trained machine learning modelto the electrical signals, wherein the machine learning model is trainedon training examples including electrical signals of human hearts andknown source regions of the heart rhythm disorder.

In some embodiments, the techniques described herein relate to a methodfor guiding an internal catheter using a body surface device, the methodincluding: receiving a first set of signal data for electrical signalsof a heart detected by a plurality of sensing electrodes carried on abody surface device worn by a subject, the electrodes covering one ormore spatial projections of one or more areas of a heart projected on abody surface of the subject; receiving a second set of signal data forelectrical signals of the heart detected by an internal catheterpositioned within the heart or in contact with the heart; conducting adirectionality analysis using the first set and the second set of signaldata; and guiding a movement of the internal catheter towards a targettissue to treat a heart rhythm disorder based on the directionalityanalysis.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes inputting aversion of the first set of signal data and a version of the second setof signal data to one or more machine learning models to generate adirectional vector.

In some embodiments, the techniques described herein relate to a method,wherein the one or more machine learning models are trained based ontraining samples with output labels that monitor one or more of thefollowing: whether a treatment was acutely successful, whether atreatment produced freedom from arrhythmia for at least a thresholdperiod of time, and/or whether a treatment produced a good quality oflife based on clinical determination.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes: generating afirst directional vector from the first set of signal data detected bythe body surface device; generating a second directional vector from thesecond set of signal data detected by the internal catheter; andgenerating a final directional vector that guides the movement of theinternal catheter based on the first directional vector and the seconddirectional vector.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes: comparingspatial activation patterns between data from the body surface deviceand data from the internal catheter.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes: comparingtiming information between data from the body surface device and datafrom the internal catheter.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes: generating afirst directional vector from the first set of signal data detected bythe body surface device; generating a second directional vector from thesecond set of signal data detected by the internal catheter; determiningthat the first directional vector and the second directional vector aredissimilar; and generating a guidance on the movement of the internalcatheter using past records of the subject and data from a database ofprocedures.

In some embodiments, the techniques described herein relate to a method,wherein the body surface device records from a surface area that is lessthan 200 cm2.

In some embodiments, the techniques described herein relate to a method,wherein the directionality analysis is conducted without an anatomicalthree dimensional reconstruction.

In some embodiments, the techniques described herein relate to a method,wherein conducting the directionality analysis includes: identifying aregion of interest by the first set of signal data detected by the bodysurface device; determining a number of a second set of electrodescarried by the internal catheter that overlap with the region ofinterest to determine an area overlap; and causing, responsive to thearea overlap being higher than a threshold, the internal catheter tomodify a tissue region at the region of interest.

In some embodiments, the techniques described herein relate to atreatment system for providing therapy to treat a heart rhythm disorder,the treatment system including: a body surface device configured to beworn by a subject, the body surface device including a plurality ofsensing electrodes configured to detect electrical signals of a heart ofthe subject to generate a first set of signal data, the electrodescovering one or more spatial projections of one or more areas of a heartprojected on a body surface of the subject; an internal catheterconfigured to be positioned within the heart or in contact with theheart, the internal catheter configured to detect electrical signals ofthe heart to generate a second set of signal data; and a computingdevice configured to: conduct a directionality analysis using the firstset and the second set of signal data; and guide a movement of theinternal catheter towards a target tissue to treat a heart rhythmdisorder based on the directionality analysis.

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes inputting aversion of the first set of signal data and a version of the second setof signal data to one or more machine learning models to generate adirectional vector.

In some embodiments, the techniques described herein relate to a system,wherein the one or more machine learning models are trained based ontraining samples with output labels that monitor one or more of thefollowing: whether a treatment was acutely successful, whether atreatment produced freedom from arrhythmia for at least a thresholdperiod of time, and/or whether a treatment produced a good quality oflife based on clinical determination.

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes: generating afirst directional vector from the first set of signal data detected bythe body surface device; generating a second directional vector from thesecond set of signal data detected by the internal catheter; andgenerating a final directional vector that guides the movement of theinternal catheter based on the first directional vector and the seconddirectional vector.

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes: comparingspatial activation patterns between data from the body surface deviceand data from the internal catheter

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes: comparingpatterns of spatial activation between the body surface device and theinternal catheter.

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes: generating afirst directional vector from the first set of signal data detected bythe body surface device; generating a second directional vector from thesecond set of signal data detected by the internal catheter; determiningthat the first directional vector and the second directional vector aredissimilar; and generating a guidance on the movement of the internalcatheter using past records of the subject and data from a database ofprocedures.

In some embodiments, the techniques described herein relate to a system,wherein the body surface device records from a surface area that is lessthan 200 cm2.

In some embodiments, the techniques described herein relate to a system,wherein the directionality analysis is conducted without an anatomicalthree dimensional reconstruction.

In some embodiments, the techniques described herein relate to a system,wherein conducting the directionality analysis includes: identifying aregion of interest by the first set of signal data detected by the bodysurface device; determining a number of a second set of electrodescarried by the internal catheter that overlap with the region ofinterest to determine an area overlap; and causing, responsive to thearea overlap being higher than a threshold, the internal catheter tomodify a tissue region at the region of interest.

In some embodiments, the techniques described herein relate to acomputing device for controlling treatment of a heart rhythm disorder bya treatment probe, the computing device including: a processor; andmemory, the memory, when executed by the processor, causes the processorto perform operations including: receiving a first set of signal datafor electrical signals of a heart detected by a plurality of sensingelectrodes carried on a body surface device worn by a subject, theelectrodes covering one or more spatial projections of one or more areasof a heart projected on a body surface of the subject; receiving asecond set of signal data for electrical signals of the heart detectedby an internal catheter positioned within the heart or in contact withthe heart; conducting a directionality analysis using the first set andthe second set of signal data; and guiding a movement of the internalcatheter towards a target tissue to treat a heart rhythm disorder basedon the directionality analysis.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes inputting a version of the first set of signal data and aversion of the second set of signal data to one or more machine learningmodels to generate a directional vector.

In some embodiments, the techniques described herein relate to acomputing device, wherein the one or more machine learning models aretrained based on training samples with output labels that monitor one ormore of the following: whether a treatment was acutely successful,whether a treatment produced freedom from arrhythmia for at least athreshold period of time, and/or whether a treatment produced a goodquality of life based on clinical determination.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes: generating a first directional vector from the first set ofsignal data detected by the body surface device; generating a seconddirectional vector from the second set of signal data detected by theinternal catheter; and generating a final directional vector that guidesthe movement of the internal catheter based on the first directionalvector and the second directional vector.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes: comparing spatial activation patterns between data from thebody surface device and data from the internal catheter.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes: comparing timing information between data from the bodysurface device and data from the internal catheter.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes: generating a first directional vector from the first set ofsignal data detected by the body surface device; generating a seconddirectional vector from the second set of signal data detected by theinternal catheter; determining that the first directional vector and thesecond directional vector are dissimilar; and generating a guidance onthe movement of the internal catheter using past records of the subjectand predetermined clinical rules.

In some embodiments, the techniques described herein relate to acomputing device, wherein the body surface device records from a surfacearea that is less than 200 cm2.

In some embodiments, the techniques described herein relate to acomputing device, wherein the directionality analysis is conductedwithout an anatomical three dimensional reconstruction.

In some embodiments, the techniques described herein relate to acomputing device, wherein conducting the directionality analysisincludes: identifying a region of interest by the first set of signaldata detected by the body surface device; determining a number of asecond set of electrodes carried by the internal catheter that overlapwith the region of interest to determine an area overlap; and causing,responsive to the area overlap being higher than a threshold, theinternal catheter to modify a tissue region at the region of interest.

Additional Considerations

The foregoing description of the embodiments has been presented for thepurpose of illustration; it is not intended to be exhaustive or to limitthe patent rights to the precise forms disclosed. Persons skilled in therelevant art can appreciate that many modifications and variations arepossible in light of the above disclosure.

Any feature mentioned in one claim category, e.g. method, can be claimedin another claim category, e.g. computer program product, system,storage medium, as well. The dependencies or references back in theattached claims are chosen for formal reasons only. However, any subjectmatter resulting from a deliberate reference back to any previous claims(in particular multiple dependencies) can be claimed as well, so thatany combination of claims and the features thereof is disclosed and canbe claimed regardless of the dependencies chosen in the attached claims.The subject-matter may include not only the combinations of features asset out in the disclosed embodiments but also any other combination offeatures from different embodiments. Various features mentioned in thedifferent embodiments can be combined with explicit mentioning of suchcombination or arrangement in an example embodiment or without anyexplicit mentioning.

Furthermore, any of the embodiments and features described or depictedherein may be claimed in a separate claim and/or in any combination withany embodiment or feature described or depicted herein or with any ofthe features.

Some portions of this description describe the embodiments in terms ofalgorithms and symbolic representations of operations on information.These operations and algorithmic descriptions, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as engines, withoutloss of generality. The described operations and their associatedengines may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software engines,alone or in combination with other devices. In some embodiments, asoftware engine is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described. The term “steps” doesnot mandate or imply a particular order. For example, while thisdisclosure may describe a process that includes multiple stepssequentially with arrows present in a flowchart, the steps in theprocess do not need to be performed by the specific order claimed ordescribed in the disclosure. Some steps may be performed before otherseven though the other steps are claimed or described first in thisdisclosure. Likewise, any use of (i), (ii), (iii), etc., or (a), (b),(c), etc. in the specification or in the claims, unless specified, isused to better enumerate items or steps and also does not mandate aparticular order.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein. In addition, the term “each” used in thespecification and claims does not imply that every or all elements in agroup need to fit the description associated with the term “each.” Forexample, “each member is associated with element A” does not imply thatall members are associated with an element A. Instead, the term “each”only implies that a member (of some of the members), in a singular form,is associated with an element A. In claims, the use of a singular formof a noun may imply at least one element even though a plural form isnot used.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the patent rights. It istherefore intended that the scope of the patent rights be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsis intended to be illustrative, but not limiting, of the scope of thepatent rights.

1. A method for guiding an internal catheter using a body surfacedevice, the method comprising: receiving a first set of signal data forelectrical signals of a heart detected by a plurality of sensingelectrodes carried on a body surface device worn by a subject, theelectrodes covering one or more spatial projections of one or more areasof a heart projected on a body surface of the subject; receiving asecond set of signal data for electrical signals of the heart detectedby an internal catheter positioned within the heart or in contact withthe heart; conducting a directionality analysis using the first set andthe second set of signal data; and guiding a movement of the internalcatheter towards a target tissue to treat a heart rhythm disorder basedon the directionality analysis.
 2. The method of claim 1, whereinconducting the directionality analysis comprises inputting a version ofthe first set of signal data and a version of the second set of signaldata to one or more machine learning models to generate a directionalvector.
 3. The method of claim 2, wherein the one or more machinelearning models are trained based on training samples with output labelsthat monitor one or more of the following: whether a treatment wasacutely successful, whether a treatment produced freedom from arrhythmiafor at least a threshold period of time, and/or whether a treatmentproduced a good quality of life based on clinical determination.
 4. Themethod of claim 1, wherein conducting the directionality analysiscomprises: generating a first directional vector from the first set ofsignal data detected by the body surface device; generating a seconddirectional vector from the second set of signal data detected by theinternal catheter; and generating a final directional vector that guidesthe movement of the internal catheter based on the first directionalvector and the second directional vector.
 5. The method of claim 1,wherein conducting the directionality analysis comprises: comparingspatial activation patterns between data from the body surface deviceand data from the internal catheter.
 6. The method of claim 1, whereinconducting the directionality analysis comprises: comparing timinginformation between data from the body surface device and data from theinternal catheter.
 7. The method of claim 1, wherein conducting thedirectionality analysis comprises: generating a first directional vectorfrom the first set of signal data detected by the body surface device;generating a second directional vector from the second set of signaldata detected by the internal catheter; determining that the firstdirectional vector and the second directional vector are dissimilar; andgenerating a guidance on the movement of the internal catheter usingpast records of the subject and data from a database of procedures. 8.The method of claim 1, wherein the body surface device records from asurface area that is less than 200 cm².
 9. The method of claim 1,wherein the directionality analysis is conducted without an anatomicalthree dimensional reconstruction.
 10. The method of claim 1, whereinconducting the directionality analysis comprises: identifying a regionof interest by the first set of signal data detected by the body surfacedevice; determining a number of a second set of electrodes carried bythe internal catheter that overlap with the region of interest todetermine an area overlap; and causing, responsive to the area overlapbeing higher than a threshold, the internal catheter to modify a tissueregion at the region of interest.
 11. A treatment system for providingtherapy to treat a heart rhythm disorder, the treatment systemcomprising: a body surface device configured to be worn by a subject,the body surface device comprising a plurality of sensing electrodesconfigured to detect electrical signals of a heart of the subject togenerate a first set of signal data, the electrodes covering one or morespatial projections of one or more areas of a heart projected on a bodysurface of the subject; an internal catheter configured to be positionedwithin the heart or in contact with the heart, the internal catheterconfigured to detect electrical signals of the heart to generate asecond set of signal data; and a computing device configured to: conducta directionality analysis using the first set and the second set ofsignal data; and guide a movement of the internal catheter towards atarget tissue to treat a heart rhythm disorder based on thedirectionality analysis.
 12. The system of claim 11, wherein conductingthe directionality analysis comprises inputting a version of the firstset of signal data and a version of the second set of signal data to oneor more machine learning models to generate a directional vector. 13.The system of claim 12, wherein the one or more machine learning modelsare trained based on training samples with output labels that monitorone or more of the following: whether a treatment was acutelysuccessful, whether a treatment produced freedom from arrhythmia for atleast a threshold period of time, and/or whether a treatment produced agood quality of life based on clinical determination.
 14. The system ofclaim 11, wherein conducting the directionality analysis comprises:generating a first directional vector from the first set of signal datadetected by the body surface device; generating a second directionalvector from the second set of signal data detected by the internalcatheter; and generating a final directional vector that guides themovement of the internal catheter based on the first directional vectorand the second directional vector.
 15. The system of claim 11, whereinconducting the directionality analysis comprises: comparing spatialactivation patterns between data from the body surface device and datafrom the internal catheter.
 16. The system of claim 11, whereinconducting the directionality analysis comprises: comparing patterns ofspatial activation between the body surface device and the internalcatheter.
 17. The system of claim 11, wherein conducting thedirectionality analysis comprises: generating a first directional vectorfrom the first set of signal data detected by the body surface device;generating a second directional vector from the second set of signaldata detected by the internal catheter; determining that the firstdirectional vector and the second directional vector are dissimilar; andgenerating a guidance on the movement of the internal catheter usingpast records of the subject and data from a database of procedures. 18.The system of claim 11, wherein the body surface device records from asurface area that is less than 200 cm².
 19. The system of claim 11,wherein the directionality analysis is conducted without an anatomicalthree dimensional reconstruction.
 20. The system of claim 11, whereinconducting the directionality analysis comprises: identifying a regionof interest by the first set of signal data detected by the body surfacedevice; determining a number of a second set of electrodes carried bythe internal catheter that overlap with the region of interest todetermine an area overlap; and causing, responsive to the area overlapbeing higher than a threshold, the internal catheter to modify a tissueregion at the region of interest.
 21. A computing device for controllingtreatment of a heart rhythm disorder by a treatment probe, the computingdevice comprising: a processor; and memory, the memory storinginstructions, the instructions, when executed by the processor, causingthe processor to perform operations comprising: receiving a first set ofsignal data for electrical signals of a heart detected by a plurality ofsensing electrodes carried on a body surface device worn by a subject,the electrodes covering one or more spatial projections of one or moreareas of a heart projected on a body surface of the subject; receiving asecond set of signal data for electrical signals of the heart detectedby an internal catheter positioned within the heart or in contact withthe heart; conducting a directionality analysis using the first set andthe second set of signal data; and guiding a movement of the internalcatheter towards a target tissue to treat a heart rhythm disorder basedon the directionality analysis.
 22. The computing device of claim 21,wherein conducting the directionality analysis comprises inputting aversion of the first set of signal data and a version of the second setof signal data to one or more machine learning models to generate adirectional vector.
 23. The computing device of claim 22, wherein theone or more machine learning models are trained based on trainingsamples with output labels that monitor one or more of the following:whether a treatment was acutely successful, whether a treatment producedfreedom from arrhythmia for at least a threshold period of time, and/orwhether a treatment produced a good quality of life based on clinicaldetermination.
 24. The computing device of claim 21, wherein conductingthe directionality analysis comprises: generating a first directionalvector from the first set of signal data detected by the body surfacedevice; generating a second directional vector from the second set ofsignal data detected by the internal catheter; and generating a finaldirectional vector that guides the movement of the internal catheterbased on the first directional vector and the second directional vector.25. The computing device of claim 21, wherein conducting thedirectionality analysis comprises: comparing spatial activation patternsbetween data from the body surface device and data from the internalcatheter.
 26. The computing device of claim 21, wherein conducting thedirectionality analysis comprises: comparing timing information betweendata from the body surface device and data from the internal catheter.27. The computing device of claim 21, wherein conducting thedirectionality analysis comprises: generating a first directional vectorfrom the first set of signal data detected by the body surface device;generating a second directional vector from the second set of signaldata detected by the internal catheter; determining that the firstdirectional vector and the second directional vector are dissimilar; andgenerating a guidance on the movement of the internal catheter usingpast records of the subject and data from a database of procedures. 28.The computing device of claim 21, wherein the body surface devicerecords from a surface area that is less than 200 cm².
 29. The computingdevice of claim 21, wherein the directionality analysis is conductedwithout an anatomical three dimensional reconstruction.
 30. Thecomputing device of claim 21, wherein conducting the directionalityanalysis comprises: identifying a region of interest by the first set ofsignal data detected by the body surface device; determining a number ofa second set of electrodes carried by the internal catheter that overlapwith the region of interest to determine an area overlap; and causing,responsive to the area overlap being higher than a threshold, theinternal catheter to modify a tissue region at the region of interest.